{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from MA 3-23-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)         \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ########################\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections only count if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     \"\"\"\n",
    "    #global borderUnits\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        trySet = set(list())\n",
    "        for U in addedList:\n",
    "            trySet = trySet.union(set(unitNbrs[U] ) )\n",
    "        tryList = list( trySet.difference(set(addedList)) )\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP and wontEnclave(UU, addedList, unitNbrs, borderUnits) :\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  MD\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use md_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. MD has 6177224 peeps and is divided into 2042 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 24 counties in MD .  I will assign them county Nos 0 - 23\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 6 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 6 units will be axed\n",
      "here is the proposed skipList [1874, 1939, 1270, 1364, 1497, 1771]\n",
      "here is the final skipList [1939, 1364]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "skipList = [1939, 1364] #...  #alter here if needed - 1874, 1270, 1497, 1771 create contiguity problems if axed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a5f56f1b-ea54-4746-96b8-eb82602529b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(tractGeom[1874])\n",
    "plotPoly(tractGeom[1270])\n",
    "plt.show()\n",
    "plotPoly(tractGeom[1364])\n",
    "plotPoly(tractGeom[1939])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 6\n",
      "removing coastal units from countyNo 7\n",
      "removing coastal units from countyNo 11\n",
      "removing coastal units from countyNo 17\n",
      "removing coastal units from countyNo 18\n",
      "removing coastal units from countyNo 22\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.77788\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 1388 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 1392 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 1400 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block identifies whole districts to be cut out of the map\n",
      "For MD my input file says to cut out 0 districts out of 8\n",
      "There were no cut districts in MD\n"
     ]
    }
   ],
   "source": [
    "# updated NCUTDISTRICTS (district slice-out) code vs. vanillaRefine - this WORKS\n",
    "print(\"This code block identifies whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02f95c46-5073-4b9d-817c-16a57d5ebb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before skewing outcroppings.\n",
      "Of the total statePop 6177224.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 2 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 8 rather than original 8\n",
      "Each district will be drawn to house 772153.0 = 1/ 8 of non-cutout state pop 6177224.0 vs original= 6177224.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "here is the MD map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before skewing outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 12\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "the max LAT-scaled distance between counties is 3.38627\n"
     ]
    },
    {
     "data": {
      "image/png": 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trdm8eTM5OTlMmjQJpVK8rTYW8QonCC2MXq8nIiKC3bt3M2TIEMaOHSuSFUEwkl69ejF9+nQuXLjATz/9REFBgbFDarXEq5wgtCBFRUX8+uuvHD9+nHHjxjFo0CAxE0gQjCwgIIA5c+aQkpLC999/T3Z2trFDapVEwiIILUR+fj4//fQTly9fZsaMGfTo0cPYIQmC8CcvLy8eeeQR8vLy+Oabb7h7966xQ2p1RMIiCC1AZmYm33//Pbdv32bOnDn4+/sbOyRBEO7Rpk0b5s2bh0ql4ttvvyU+Pt7YIbUqImERhGbu9u3bfPvttxQWFvLoo4/i6elp7JAEQaiEjY0NjzzyCE5OTqxYsYKLFy8aO6RWQyQsgtCM3bp1i++//x4zMzMee+wxnJycjB2SIAjVsLCwYPbs2fj4+LB69WrOnTtn7JBaBZGwCEIzlZ6ezk8//YRareaRRx4Ra2QJQgtiYmLC9OnTCQgIYN26dVy/ft3YIbV4ImERhGYoJyeHH3/8EaVSyUMPPYS5ubmxQxIEoZbkcjmTJk3Cy8uLVatWkZSUZOyQWjSRsAhCM1NYWMjPP/9MYWEhs2fPxtra2tghCYJQR0qlkhkzZuDo6MhPP/1EamqqsUNqsUTCIgjNiFarZfXq1aSlpfHQQw9hb29v7JAEQagnU1NTHnzwQczNzfnxxx/JysoydkgtkkhYBKGZ0Ov1rF+/nps3bzJz5kxcXV2NHZIgCA3E0tKS2bNno9Pp+Omnn8jPzzd2SC2OSFgEoRmQJInNmzdz/vx5pk6dire3t7FDEgShgdna2jJ79myysrJYuXIlGo3G2CG1KCJhEYRmIDIykhMnTjBu3Dg6depk7HAEQWgkzs7OPPjgg6SkpLB27Vp0Op2xQ2oxRMIiCEZ25MgR9u7dy7Bhw+jevbuxwxEEoZG1bduW6dOnc+XKFf744w8kSTJ2SC2CSFgEwYhiY2PZsmULoaGh9O/f39jhCILQRHx9fZk4cSKnT59mx44dxg6nRVAaOwBBuF9duXKFDRs2EBwczIgRI8Sqy4JwnwkODiYvL4+tW7diaWlJv379jB1SsyYSFkEwgoSEBH755Rc6dOjAhAkTkMtFY6cg3I9CQkLIzc1lx44dWFhY0K1bN2OH1GyJhEUQmlhaWho///wzLi4uTJs2DYVCYeyQBEEwoqFDh5KXl8cff/yBtbU1Pj4+xg6pWRIf6wShCWm1WtauXYuZmRkzZ85EpVIZOyRBEIxMJpMxZswYOnTowPr168nMzDR2SM1SvRKWxYsXI5PJeOGFFwzbJEnizTffxM3NDXNzcwYPHszZs2ervda6desICAjA1NSUgIAANmzYUJ/QBKFZ2rFjB7du3WLatGlYWFgYOxxBEJqJknWHlEol69atE9OdK1DnhOXYsWN89dVXBAcHl9n+3nvv8eGHH7J06VKOHTuGi4sLI0aMIDs7u9JrHTp0iBkzZjB79mxOnz7N7NmzmT59OkeOHKlreILQ7Jw7d44jR44QFhaGm5ubscMRBKGZsbS0ZOrUqcTHx7Nnzx5jh9Ps1ClhycnJ4cEHH+Trr7/Gzs7OsF2SJD766CNeffVVJk+eTGBgICtWrCAvL4+VK1dWer2PPvqIESNGsGjRIvz9/Vm0aBHDhg3jo48+qkt4gtDspKWl8fvvvxMQEECvXr2MHY4gCM2Up6cnw4YNIyoqisuXLxs7nGalTgnLM888w5gxYxg+fHiZ7devXyclJYWRI0catpmamjJo0CAOHjxY6fUOHTpU5hyAsLCwKs8pLCwkKyurzJcgNEcl41YsLCwYP368mL4sCEKV+vbti4+PDxs2bBDvbaXUOmFZvXo1J0+eZPHixeX2paSkANCmTZsy29u0aWPYV5GUlJRan7N48WJsbGwMX23btq3NwxCEJlN63IqZmZmxwxEEoZkrGc+iUChE+f5SapWwxMfH8/zzz/PTTz9V+cJ77ydISZKq/VRZ23MWLVpEZmam4Ss+Pr4Gj0AQmtb58+c5cuQII0eOFONWBEGosdLjWSIjI40dTrNQq4TlxIkT3L59mx49eqBUKlEqlezdu5dPPvkEpVJpaCW5t2Xk9u3b5VpQSnNxcan1OaampqjV6jJfgtCcpKen89tvv9GpUyd69+5t7HAEQWhhvLy8GDp0KPv37+fKlSvGDsfoapWwDBs2jNjYWKKjow1fPXv25MEHHyQ6Opr27dvj4uJSZl0EjUbD3r176du3b6XXDQ0NLbeWwvbt26s8RxCaM61Wy6+//oq5ubkYtyIIQp3169cPHx8f1q9ff9+PZ6lVwmJtbU1gYGCZL0tLSxwcHAgMDDTUZPnPf/7Dhg0bOHPmDA8//DAWFhbMmjXLcJ05c+awaNEiw7+ff/55tm/fzpIlS7hw4QJLlixh586dZeq7CEJLsnPnTlJSUpg2bRrm5ubGDkcQhBaq9HiW+70+S4NXul24cCEvvPACTz/9ND179iQxMZHt27djbW1tOObmzZskJycb/t23b19Wr17N999/T3BwMMuXL+eXX36hT58+DR2eIDS68+fPc/jwYUaOHIm7u7uxwxEEoYUrGc9y8+bN+3o8i0ySJMnYQTSErKwsbGxsyMzMFONZBKNJT0/nyy+/xNvbmxkzZoiuIEEQGsz+/fvZtWsXDz30UKtab6im799iLSFBaCA6nc6wTtCECRNEsiIIQoPq16+fYb2hqqrHt1YiYRGEBnLkyBGSkpKYOnWqGLciCEKDk8vlTJ48GZlMxrZt24wdTpMTCYsgNICsrCwiIyPp1asXHh4exg5HEIRWytLSkhEjRnDmzBmuXbtm7HCalEhYBKEBbNu2DRMTE4YMGWLsUARBaOW6dOmCp6cnERERaLVaY4fTZETCIgj1dO3aNc6ePcvIkSNFV5AgCI1OJpMxZswYUlNTOXTokLHDaTIiYRGEetBqtWzevBkvLy+Cg4ONHY4gCPeJNm3aEBISwr59+8jIyDB2OE1CJCyCUA+HDh0iLS2N8PBwMStIEIQmNXjwYMzMzNi6dauxQ2kSImERhDrKyMhg7969hISEVLnulSAIQmMwNTVl5MiRXLhwgcuXLxs7nEYnEhZBqKOtW7dibm7O4MGDjR2KIAj3qcDAQNq1a0dERARFRUXGDqdRiYRFEOrg0qVLXLhwgbCwMExNTY0djiAI9ymZTEZ4eDiZmZkcOHDA2OE0KpGwCEItFRUVsWXLFtq3b0/nzp2NHY4gCPc5Jycn+vbty/79+0lLSzN2OI1GJCyCUEtRUVFkZmaKgbaCIDQbAwcOxMrKii1bttBKlggsRyQsglALaWlpREVF0bdvXxwdHY0djiAIAgAqlYpRo0Zx+fJlLly4YOxwGoVIWAShhiRJIiIiAisrKwYOHGjscARBEMrw9/fHx8eHrVu3otFojB1OgxMJiyDU0IULF7hy5QqjRo1CpVIZOxxBEIQySgbg5uTksG/fPmOH0+BEwiIINaDX69m1axcdOnTA39/f2OEIgiBUyN7enn79+nH48GFycnKMHU6DEgmLINTAhQsXuHv3LoMHDxYDbQVBaNZCQ0ORy+Wtbp0hkbAIQjUkSWL//v20a9eOtm3bGjscQRCEKpmbm9OrVy+OHTtGfn6+scNpMCJhEYRqXL16leTkZAYMGGDsUARBEGokNDQUvV7P0aNHjR1KgxEJiyBUY9++fbi7u9OuXTtjhyIIglAjVlZWdOvWjcOHD1NYWGjscBqE0tgBtAR5WZns+Wk5msJCJEkqLsojSUhISBLF3//57+LvS28r9f2fxXwM55cq7vPnkVC63s+fYyXK1QAqGUIhld9m2FTyzT3jLSqqJ6TTSyjksnLHlrtfuc0V7KjN+A7ZvUHX/Bolh5QeTyIr9U1JbMW7//w5aguxURaVi7ry28nI1su4WWSKj1LD5iWL/nrMpW9W4UVK7l/Zz6j89r+OLbWvVPyl71nxOBpZ2eNL30t27zaQyeTIZDJkcgUy+V/HyWQVf2/4uRrOL33fih+vTFZqX6n7lz6uTJGrkufIPf8ud1wl55WcWeZQwzX/eo4ZvteX2if9+Z10z3XKnS+VejSlt1HuOa+XSfecV/L8x3BNw+OQpPKvA1Lxj0+hUIJSgbagAJlej0LSl/v5lD2xgh9NbY6592dd0Tl6BcgUpU6555gKXmyqO6bCemeV/t7L/44MkZb8p9TPWSaT0W/WbDwCAiu4SevUr18/Tpw4wcmTJwkNDTV2OPUmEpYaSL58kQt7d2Dj5YNcaWJ4E5Td8yIuk8nu2ffXC7NMJkMul1d+fPH/KmXYJUll3nwML5uSVP59rbJrQPETWQYpWQWoFApsLVV/XedetayaWGmVxUq2l375/2tLZdcp/SJU/p6GF6tSL1QlpxTkZJCal4qJlS2mlrY1iFEiUTLBFD0qrZZ87b2HlrpRFdcoG/a9L9gVbKviTbfMNctcvrI3AqnU4fe8+ZdKpPX6e94AS79JS/fGU/7fWm0RRcjQm1jdE1jp30vZbWVUlFBVsC+3sBAbhfyehNRw4D3/uzfx++ubis4zJFEVHlvxtcv87d6TtGXlpGNhZ3vPY5Ld8+9SwchkZa5V+neq02pBp0dpaoo+9xZqS5e/or83ea0gmS23paJjyh9U5X5dRiYyW9d7jqk6FpmsfLJdPtev/oNQ6eT+r2/vTZplZX6nV08e4UbsqfsqYbG1tSU4OJiDBw/Sq1cvlMqW/ZbfsqNvMsUvHDNfeQNLWzsjx9JwdDo97269wKtjAowdSpNJuHmNlJObcekahod3xyqPTUpK4uxXXzF58mSCg4OR9HpyMtIws7TCxNSsiSJu/q5dP4k2L42OnYc36n12nD1P7J1Unhzcv1Hv0xCKijRs2fMz40c+0uDXPnv4EzqHPNfg160tzdFdKNzaofBob+xQauSrJx7+KzG/j/Tr14/o6Giio6Pp2bOnscOpFzGGpQZa6bIMfLHvGo/2a1erczZt2oSfnx++vr5888035fYfPXqUzp074+Pjw7/+9S/D9t27d9OtWze6dOnCyJEjDQt0nT59mpCQELp27Uq/fv24du1a/R5UNTw829Nz4t9JPrQGva7qF6+oqCjs7Ozo3Lkzd25c54f5j/PVUw/z6cPTWPPqc+z44n+c2rqR5MsX0bbCqpI1cSf5Ajnp8Y2arOQWFPDtgcPcyctDBuhawJvO3iN/ENp7tLHDaFSq3sPQxuxD0hYZO5QakcnlSC3gb6ehOTk5ERAQwIEDB9DpdMYOp15EwlITf2Ysran+xp2sQvR6CVdb8xqfo9VqmT9/Prt37+bkyZMsWbKk3MqgzzzzDKtWreLChQts3LiRM2fOAPD888+zevVqTp8+Tbdu3fjyyy8BeO2113jrrbeIjo7moYceYsmSJQ33ICtxO+Eazld++XPcRsXu3LnDuXPn6NElmKSL51jzxkvIZDLGL3iFoQ8/icrSiuQLsUT+8DUrX1vApw9PZeXCJzm19Y/75kXx6NF1nI3ZQnD3CY16H1OViiytjpk9uvGPwf1RyJv/y1ZObiZOti7VH9jCqYZMQxOxHH1u8y9QJpfJ78sWFoABAwaQnp7O2bNnjR1KvYguodpoRQnLN1HXeHGkX63OKWk9cXd3ByA8PJxt27Yxc+ZMoLgLRavVEhwcDMCsWbPYuHEjgYGByGQysrOzAcjJyaFTp04AZbZnZ2fj6up6720bzN3kOK7t+h6VW2f0IYvwquL3uf6H75EVaTj8xYcckSTsnB2Z/vb/MLMqHqPRNWwMANqiIu7ejCP58gXiY46z+/uviN32Bx1696fXxOmozC0aLP5NmzaxYMEC9Ho9L7/8MvPmzTPsy8vLY+rUqVy7dg2lUskTTzzB3//+9wa7d0V6955C7KlNpGenYmft0Gj3UchkqBQKdFotyhawJEJCyjXs7dsYO4wmITO3RBX+CJq965A7emPSpY+xQ6qUTC5HauEtDHXl6uqKj48PUVFRBAYGFo+nbIFEwlIDFY2Qb8mOXE/Fz8UaE2Xt/miTkpIMyQqAh4cHiYmJVe7fu3cvAMuWLTOswdOhQwc++eQTAN577z3CwsJ44YUXsLKyapSaAXptERcP/IbSri3a/Ex6DpqCXKGo9PiMjAxSsvNQpd0ioN8guoaNwcmrXYXjVpQmJrh08MWlgy/dRo3jRkw0MVs3cHzzb1w6tI/xC9/EwcOz3o+hpHVrz549qNVqunfvzuTJk7G3tzcc8/LLLzNo0CByc3Pp2bMno0ePxsfHp973rkxmWgL5en2jJStJd+/yw/Fo3G1tGNupY4tIVgBOnN7DuBENP3alhAwZekmPXNY83nRkSiWmw2agvRxD4dafUA2agsy85i23TUUuV9y3LSxQ3Mry/fffc+nSpRa7vEjz+Itv7lrZIJad524xubtHrc+raNZObkY6h9etRqctqnB/STfa//73P3bs2EFSUhKhoaEsXrwYgM8//5xly5aRkJDA3//+d+bPn1/ruKqTlp5G5pltZFyMwif8+SqTFYDtmzch0+swSb+DT88Q3Dp2qvEgW6/groxb+BYPLfkUmQx++r/nuHbqWL0fQ+nWLWtra0PrVgkLCwsGDRoEgKWlJb6+viQnJ9f7vpXR6nRcOreTXt3HNdo91p27yIsjBjM7pBdeDvbVn9AM6PV6JGjUT7AqpYoCbUGjXb+ulL7BqIZORxP5K7rkm8YOp5ziFpb7N2Hx8vLC09OT/fv3Vz6Ts5kTCUsNZKemAsV/8C3dL8duEh5Ut24Xd3d3Q4vKrWtXOLl/L5d2RnBgzU/En4kpsx8gISEBV1dX7ty5w/nz5+nWrRsA06ZN4+DBgwCsWrWK8PBwAKZPn27Y3pCszC3JV/cl/updjmz4iey0u5Ueu3/tKs5dvISdroA5735Ex9C6zUhxcG/Lg0s+xzMgkN/fe5vjf6yr14tEda1bpcXHxxMTE0P37t3rfL/qxJz8jYDuExp0XNftjAyKdMVzx88m38LTyhKlomU1Ah+L2U1w576Neg9TEwtyCzIa9R51JVOpMB09B23MXmOHUo5MLkevvz+7hEoMGDCAxMRErl+/buxQ6qTlvwM3gZJBlGaWVtUc2bwVFum4fjePbp51m5rdu3dvzpw5w4n9kXzz4rPs3LMHPxcnAJzb+6AozEehUBATE4NWq2XVqlWMGzcOOzs77ty5Y3iS7Nq1Cz+/4vEzDg4OHD58uNz2hpCacJOd333Jhvf+xclN67l+6gyX9h/hq6ceZvlzj7D/5+9JuXLJkEic2bOD/ZGRKBQKHl30Bs7e9ZuuaWJmxviFb9ItLJy9P3/Pvh+/rvO1qmq9Kq2goIAZM2bw3//+F0tLyzrfryKJCWeIid7MsYM/4dq2C5YWDTvF/5uTMSzdf4hj12+w8sQp+vp0qPU1qprFlpeXR3h4OP7+/gQGBvLpp58a9s2aNQs/Pz8CAwNZtGhRueu++OKLODo6Vnv/lFs3ae/RqdZx14bKzI78vMqT7uZA3jGUojPVL7xX3azDZ555Bmdn53LTcR966CG6dOlCUFAQTz31VLmunop+X3KFAn3RPcWU7jM+Pj64urqyb98+Y4dSJy3r44uRSHodclOzFj9LaNneqzwxsHbTmEtTKpV88MEHjJ8yjcL8PP720INYpiWx4lgsuX+biyVahni6MmP6dDRFRcyePZugoCCguOtn3LhxKBQK3N3dWbFiBQBffvml4QXHxsaG7777rk6x7f91Fcc3rkPt5olvcBc6dO/FrpU/cudiLDKT4rEPg+f+jU79BxN/Nobrxw4QuzOCo3+sQ6lU4hUYyJXo0+g6dad3SAiWVg2TnCqUSgY//CTWjm2I/PFb7Nw8CR4+qsJjb127wrVTx/DtFYqjp7dhe2FeLo52duVar/r0KTvAUZIk5s6dS3h4OFOnTm2Q+AFupd4k6cYpFEozOnUejlavx9y04ccoBDnakafRYiaX8bd+ITiprWt1fn3G+cyZM4eVK1ei1WoZMWIEu3fvZujQoQCcO3eOlJSUau9foMlH0QSFucwsHMgsSKv+QCMyaedD4fbjEFh5ddWa/L5mzZrFo48+yhNPPFHm3M8//xy1Wg3AjBkz+P3335k0aRJQ+e/L3FpNfmZmQzy8Fksmk9G3b1/WrVvH3bt3a5SENyciYakBSZKQNZMBbnV1IzUXCxMFdpam9brOiCGD+eaNRZzbt5spTz7JkT/WMReA4k8uLiYSP7z3Dr3GTylz3tSpUyt8Ex00aBCnTp2qV0wAcWdi0BcWoEQietd2jv3+q2FflxHh9Agbg61LcVeYX+gA/EIHoNfruHLsMNeOHiArJR5zdw9ykNG1a9d6x3Ov7mMmkp54g13ffo6zd3tcfIqL1kl6PVdOHCHr9i2OrF9Ffk4uB9f8zEOLP6JNex902iK+fnouebl5HNl3mC0rvsOlbVs2/v47//znP8vcY9GiRVhYWPDaa681WNxpWakkXT1M155TkMmLx/6YNNjVy7I0NSM1J5VOHm516gqqbhZbZeN8fHx8GDWqOIlUKpUEBQWVSQ4XLlzI559/ztatW6u8//4jmxjQe2yt464tM1svtNd3Nfp96ksZ3I/C7T+j6NQfZVuvcvur+31BcdGzuLi4cueWJCtarZb8/PwyHyYr+31Z2NqQllBxN+r9xN/fH1NTU2JiYgxJeUvRst+Fm4her6/RlOa6FlWrrHlzxowZdO3ala5du+Lu7s7EiRPr/Bh+PnKDeQPq3rpSInLlD5zbtxuArDu3SThzGgAzh+JpnC4BXQgcMqLe96mtsU8+i5m9E1l37zDu7/MZv+AV7Nr/2e3k7mFIVkqTyxV07NOPUX9fyPR3PsUpZAht2rShTZuGn5Iqk8kY8tizOHm4s/6dV/n9P6+yden7/PL6fP747ztE/vAN+Tm5huN/WvQCP7zwKHu+W4ZOq0MhlzNraF/m/v05RkyYSA9HNee2rCc8PJykpCQSEhJYsmQJR48eNfzNlB6UW1c3Lu0tk6w0pqF+vowL6syS3ftZdewEv5+MZlPMGS7frln3R0OM88nKymLz5s0MHjwYgF9++YWePXvi6Vn9TK+8vGxsrBt/cLDKyhVd9q06n1/d69SQIUPo0qULnTt3rtHrVGXFHxUubTEd+SC6iwfR3b1d7j61+X1VZOrUqbRp0wYrKyvGjx8PVP37MrezJT8nq8bXb61MTEwICAggJiamxc2aEi0sNSDp9dUOuK1J82ZJUbWAgABCQ0OZPHkygYGBlTZv/vLLL4ZzH3roIYYPr1s10cgLt+nuaVevmQt6nY7tX3/GuT3bCR4RTpfhozCzskZhao66jStzF39Ixq1kctPTm6xsvSRJnNq6iYsnjuPi2ZYBUx/g1O6drF/8Bs5+Qcx89U3MrWrWrZCfn8/Fixcb9ROHQqlkwqK3OfLrz2SmxJN78wpIMiYufJ1rxw5x4eA+NIUaeo6bjEJpQl7abW5EH0On09JlyFAWPDmfJVotmoJ8Dqz6gejtW9i8ebPh02VjjPyXJKlJkpUSDtbWvDpiCAC7zp3nUlomt7MuY6mQ4eZQ9fTp+o7zkSSJhx9+mKeffpq2bduSm5vLJ598ws6dO6uNOy3zNhaW6mqPawgymQwkHVlZ8ajVbWt1bk1ep37//XfUajU6nY7+/fszbtw4unXrVunrVEnxx7CwMJYtW8aSJUsMhSEBTIc9QOGW75ENnI68VFdrTX9flVm7di0ajYa5c+eya9cu+vbtW+Xvy0JtQ0GuSFgAunTpwqlTp4iPj8fLq3zrV3MlEpYakPT6aruE6lNUrarmTYDCwkK2bdvG0qVLax27Xq/nwNW79V4vKDv1Dmf3bMfK0Zlhjz6B/M83sckLX+fXd15n2TPzKMxMBUli0OzH6Dl2Ur3uVxO3466xZ/mXqN29OBMfhyYjFbWHN9aOzty+GMv1U8cJGDCkRtc6f/48Op3OMOamsVjbOzL8iefLbddqNMTs2YmJSkXPsZPKrFlV3CVZ/DehUCoxt7LG3MYGpVLZqOOqUlNvYmFX++nvDWVYQCeGUfw3/OG+gxxMz+bNbp0I9vau8PiKZqnVZpzPwoULsbe3Z8GCBQBcu3aNK1euGIocpqenExwcTExMTLl7HzgaQdjgmeW2N5bAvi8SHfkmPj2fwFpd899RTbphSl6PNBoNGo3G8DdW2etUtcUfZTJUI+eg2fI9qlFzDWPKavL7qo5KpWLixIn89ttvuLi4VPn7MlerKSzIQ6fVNslYo+bM09MTGxsbYmJiWlTCIrqEakCv10M1rRN1KapWen9FzZsltmzZQmhoKLa2trWO/YdDN5jWo3afwu5VkJvD3fgbIJORc/c2d27EGfZ5BgYz86138ewciFtgN0yt1BzbGoEmP69e96wJc2trZEollja2zH7rXab/8z9Y2tmj0YP/kJH49qr5cuqnT5+mffv2hhflpuYXOoAH//M/Hv7fl+UW2JTJZOh1OooKCsi6e4eUq5e5ejQKR3e3Ro3pZtxJfNv3atR71IRcLufFwf1ZP2k0Gy7H8e2ho3yy9wCXb98pc1zJLLbExESys7OJiIggLCyszDGVjfP54osviI6OZtmyZYZtQUFB3Lp1i7i4OOLi4rCzs6swWYHi1wiVSf3Gh9WGTK4gaOCrXDn6ea3Oq2k3TN++fXF2dmb48OFlxnRV9Dr13nvvsWDBAjw8PPjuu+8qrKUkUypRjXgIzbYfDbVQavL7qohWqzWMa9HpdGzevBl/f/9qf18WahsACnKyq/9BtXJyuZygoCDOnj2LVttyZk6JhKUGatLCUl3zZnX7165dS3JyMpIksWtX2QF1a9asYcaMGbUNm5yCIu7maujoUrvZFqVdOX6EL597nN/ee9tQQC/50oUyx7h17MT4515k5mv/Yvrr/yY/9TZLH5vJiYg/yMtqvFH5akdnJr/0OqmJN/nuH09yfPtWxj/zAs8u+44xTz6HiVnNuqYyMjK4ceOGofXLWFw6+KJ2dCqzTVtUxNZP3uXTuVP4ZO5Uvn7mEX5+5R/ciU+g44CRjRZLVuZtZDJZs1u3540Rg3kstDfPDerH7vMXSbx1y/DcKpnFNmTIELp168ZLL72Eg4NDjcb5PPvss8TFxdGrVy+6du3K999/X+OYrsafw6VN/asZ15ZSaYapVRtS75yv8Tk17YY5ePAgSUlJREdHG9YDg4pfp2pa/FFmZo7JwMlotq/8M/6qf18A8+bNIzQ0lJiYGDw8PNiwYQM6nY6ZM2cSFBREly5dsLKy4sknn6z2sZtbFycsjfma1JIEBwdTUFDA5cuXjR1Kjd3f7WI1JEnVt7BU17xZWVG10ko3b44YUTxwNT8/n507d5bpE66pL/Ze46nBta9lUULS64latwalQsHYhf8k+c+aJVUNqnX2bs/DH3zG4d/XEbniKyJ/+Jrhjz1F8LBRDV5478KhKA78tg5HV3eSMtO5dngfqxNu8uj7Hxu6rGoiNjYWpVJpaEpuTtIS4zl7IAqAEX97FmsHRyxs7bCysy/XEtOQzlyMwj+w6QdP18Zj/UPZfukqO6/FMTe0+Lk2fvz4ci2UERERhu8rG+dTk0+Zd+9WPPj3zLnDjBvxcA2jbli+PZ5g78bHGD755xodX5tuGGtra4YOHcqWLVsIDAw0bL/3dWrVqlWGpTamT5/OZ599Vun95Wo7lN0GoYnahKr/2Gp/XxUNCgY4dKj6Gi/3/r7M/2w9zRcJCwDOzs64urpy+vTpZvnaV5Hm9fGpmSpuYal6rEB1zZtubm4VFlWrrHmzREREBAMGDMDaunatJOeTs2ijNsXStG45acK5M6x861VSr11i+KNP0KFHb/rPeIgBD8yudk0XezcPwp96noc/WEaHPgPY+c3nLH3yEf749APiTp/k2qljnNyykS1ffErULz+RcuUSCefPFHc71cLFw1FkxF0m6cJfnwCzUxIpzKt5d5QkSZw+fdow1a+5cfJqR+jkGSiVCo5tWIlDWy/atOvQqMkKgGlRDvYWlf/N1XWmyeDBg/H39ze0cJR48cUX8fPzIygoiEcffbRGCYRSoSC8U0dyi4xbvVQmkxltMTkTEzOsLZwoKqzZYNLqXqeysrK4fbt4Rk9hYSHbt2/H39+/ytep2hZ/VLi0BXMbtNcuVHlcQyvpEsrPFgNvSwQHB3P58mXyavGaaUy1epYtW7aM4OBg1Go1arWa0NBQtmzZYth/69YtHn74Ydzc3LCwsGDUqFHVNjctX74cmUxW7qugoPmslXH5+NFqpzXXpHlz6dKlzJw5Ez8/P8LDwwkKCqq2eXPNmjVMnz691jGvP5nAQyG1H0ylKcinICeHPT8vJ+VCLOPmL8IvpI7l6T3aMuGFl5j+xmL8+4Rw82ws6/7zTza8+xZ7ln/JuT3bOL5lIz+/Op9f3vw/fvy/F7h+6ni5T8F5WZmGasOlZWf91Rdt6+XD1Ff/zd+WflvjmUEAycnJ3L17ly5dutTpMTY2mUxG3xmzmfPBMjLuphF3+iSpCTf54R/zWPny09y9Gdeg95MkicNH1+Hu3bPSY0pmmuzevZuTJ0+yZMkS0tLKFjL7/fffOX36NDExMWzZsqVMrZ21a9cSHR1NdHS0YVtYWBjnzp0jJiaGoqIifvjhhxrHnFNYwMpjJziTkFDzB9pAos8dwLeDcf92ug9bTPSO/0OrLaz22OpepzIzMwkPDyc4OJgePXoYZglV9TpVUvyxS5cufP7557z//vvVxqHqMQDdxWNIRUX1fvw1pTK3QK5QiC6hUgIDA9Hr9Zw7d87YodRIrT5+e3h48O677xpWgF2xYgUTJkzg1KlTBAQEMHHiRExMTAzT4j788EOGDx/OuXPnqiwTrlaruXjxYpltZjUcf9AU8jVFOLtUX5ujuubNkJAQzp49W2a/qalplc2bpac211RETDIDfB3rNIPk57deI+1a8e+i27gpdOxTv3VRZDIZbQOCaBsQxLBHHic9OQmVuTnmVmqUKhV6vY4LB/ZxN/4GpyN3s/7dN1G3bY8kgV6nJT/1FnpNIQozc2zbtsetnTcqlSkOHh4U5WRj4+XDvPc+qnN8MTExWFpa0r59/crwNza1ozMAmbeSSbp0nrvJxWM3TkX8xognX2iw+1y8fooO7n44uVc+q6w+M00qU9IFCtCtW7da1eNYOKJ4Kvpbe/aTpymid/v61xuqqZsJlxg/svFWZq4JExNzOg1+gzP7/o1CZYVO0tM2YDIODhW3dFT3OnX8+PFy51T1OlXX4o+qwVPQ7PoV01Gzan1uXchkMswsrEWXUCnW1ta0b9+emJiYcssfNEe1SljGjSu7Mus777zDsmXLOHz4MCYmJhw+fJgzZ87QuXNnoHgwlrOzM6tWrWLevHmVXlcmk+Hi4lKH8JuIXk+b9j7GjqJG9Ho9pxPSWRReu2nMOm0RP//rddKuXUSuUDJ47jy6jghv0NjkcgUO7m3LbSuZejxw1sNcPXGUY9u2YGWjRi6X4+Q+DDMra3Iz0rl5+RJXTp2iKC8bbV4OchMV4557qc7x6HQ6zpw5Q2BgIIpqVnA2NrlCgb2LM0d/X1tmu0dQtwa9T+6dS/j3eaDKY2oz0yQ2Npann366TPfPrFmzUCqVzJs3j6effrrMOVqtlp9//pnPP6/d7BeAN4YM4MfDx+jYpg22lha1Pr+26lJ0a9KkSURGRjJs2DDWrl1bbv8zzzzDr7/+iqenZ5nE4erVq8yYMYOMjAyGDx/OsmXLkMlkzJgxw/Bh786dO/Tq1YvfNmzgTNS7JOiLcPEcSJt2g+v6EBuVzNwCRcdANMcPourZuAtGljC3VJN3n5fnv1eXLl1Yv3496enp2Nk1bldzfdV50K1Op+PXX38lNzeX0NBQCguLmyNLt4woFApUKhVRUVFVJiw5OTl4eXmh0+no2rUrb7/9tmFl38oUFhYa7gnFfa+NRdLrkTfzN7QSX++/zkOh3rU+7278Te5cPIOTVzumvvo2Fja2DR5bTXTo0ZsOPXpXuC+k1PdFBQXI5PJqx9NU5fr16+Tk5Bh9dlBNyGQyZi1eStKl86xf/AYA4+YvKjN1++KhKOJOHcHWxYOe44uLz9WGXq+v0WDl2sw0yc7OZurUqYbEcOXKlbi5uZGWlsaoUaPo3LmzoVw+FI9l6du3b63rcZSY2q0Lq0+exlSpYEaPro06y+lY7B4CO4VUf2Apzz33HI8++qhhLa17VbZ2zsKFC3nzzTcZO3YskyZNYvPmzYwdO7bi4pIyGYEDihdwvHjmF24f+C+6olzQa+k69O1aPsrGpWwfjCZ+Hfr0NOR2jV8l2EKtJi8to9Hv05L4+/tjYmJCTExMmedic1TrZ3NsbCxWVlaYmpry5JNPsmHDBgICAvD398fLy4tFixaRnp6ORqPh3XffJSUlheTk5Eqv5+/vz/Lly/njjz9YtWoVZmZm9OvXr9qxL4sXL8bGxsbw1bZt/WqNVEWSWkbCcjengEKtnrZ2Nf90mXjhHCe3/MHK117E3N6J6W8sNlqyUhsmZmb1SlaguDvI0dERN7fGrWfSUEwtLPAM7ELHHj0A2PjhYpY+PJXV//cMa998kU0fvcuZvXuI+uVHzu7dXevry+Vy9PmZ1bYc1GTGW4nSM00Aw8/a3t6eKVOmcOzYMcOxn332GRcuXOB///tfrWMvYW6q4pHQXoz29+XTqOpnktTH7TsJtG9bu9kVQ4YMqXIAfb9+/XC4p6KvJEkcOnSIMWPGADBnzhw2btxY5piS4pL3Lt/hFziDoH4v0nXwG2h0mlrF2lRMBkyi6ET11YQbgrmNDfmZGU1yr5ZCpVLRqVMnYmJiGqVadkOqdcLi5+dHdHQ0hw8f5qmnnmLu3LmcO3cOExMT1q1bx6VLl7C3t8fCwoLIyEhGjx5dZXN7SEiIYY2KAQMGsGbNGjp27Fhm6feKLFq0iMzMTMNXfHx8bR9Kzel1tZom21RuXbtCXMwp9Dodt65d4cv/vIf19s9Y+sRcordtrvb87cu/YfUbC9mz4mvkJiqm/d8/MbNsmFWKm7vCwkLOnz9PcHBwi1qFW6FUMm7hWzzxxQ+Mn/8KvSfPxNLRmRvni2dcWNtY02XYSDr26Ven62sVptXOeKnPTJOSqaYFBQVs27bN0H28efNmvv32W9asWYOyAaqQ5mo0tGnEcXAnz0eReqtpFtJLTU3F3t7e8HdaURdcTYpLqpSmpCQeNdRTai5kcjk00eurhZ0d+dmicNy9goODSU1NNUwQaa5q/cqgUqkMg2579uzJsWPH+Pjjj/nyyy/p0aMH0dHRZGZmotFocHJyok+fPrUazCOXy+nVq1e1LSympqZNNg21JmsJNbW8rEx+WvRCmW1mQBogVyjZ9d0yigoLsHFug2+ffuXelPOzs7i4r7jw0/yVv6PX6++rctWXLl2iqKio0UvxNxYrO3t8/xwQnZOexqVjc7BzdWPCi6/j4FH31kaFsvrnVOmZJnq9noULFxpmmnzzzTfodDomTZqERqNBr9czZcoUxo0bR25uLmFhYRQVFaHT6Zg+fTqjR48G4Pnnn6eoqIiBAwcCMG3aNF599dU6P47dl68xu0/jDCLMyE7l+PGdPD77zUa5/r1q0gVXk+KSXYf8i6vn1xEd/QPW3oPo0Hlag8ZZH3JbJ3R3UlA4Ne5YRgsbG/LFekLltG/fHisrK2JjY8uMT2tu6v0OJUlSmbEkADY2xfPdL1++zPHjx3n77Zr3m0qSRHR0dLN6I2mOY1gyb6cYvu88aDgdevZGr9Nz6/pVeo+fwvL/e4F9PxdX63Ro70fIuAn49ApFaWLC+ahIIj79L8jljHluITK5vNlVNG1s165dw9nZudkPMquJkkQzPTmJ5Que4rGPv65wderq6PU69NqalROoy0wTS0tLTpw4UeH1rly5UotIa0CqvnZSXX37xWs8+vi/qj+wgTg6OpKWlmZYU+reLrjaFJfs0GkKdJpC/I39xO59m8ABrzaLD2Myuzbo0241esJirrahIC+nzPpcQnFDga+vr2Gl7eaqVgnLK6+8wujRo2nbti3Z2dmsXr2ayMhItm7dCsCvv/6Kk5MTnp6exMbG8vzzzzNx4kRGjvyrhPicOXNwd3dn8eLFALz11luEhITg6+tLVlYWn3zyCdHR0VVWS2xqkl5vtMJQlcm8Xby8vKWdPSMef9bwpuUXWlwzZd6Hn5OXlUFqQjx716xi88fvoTC3pMvwUWTdKh5TFP7MfPz7DTTOAzCy69evV1vgqqUozM01fN+xT1+s7Kte1bgyp4+sJqDbhIYKy2g0Oi15+sbr9sjNyWLn3jVMG/9Mo92jNJlMRkhIiGGg7Q8//MCjjz5q2F+X4pJtvQZw++Y+slMvoXbyr/6ERqZPjkMZVPO1v+rK3FqNXq+jMC/3vun+rilvb29OnTpFbm5ulWVIjKlW78K3bt1i9uzZ+Pn5MWzYMI4cOcLWrVsNNRSSk5OZPXs2/v7+PPfcc8yePZtVq1aVucbNmzfLDMLNyMjg8ccfp1OnTowcOZLExET27dtH794VzxQxCr2uWbWw5GVlsv2bZSCTMWHBqxV25ShVKtSOzrTr2oOH//NfHv5gGV2GjuTUlj+4cvQgADbO1deWaY0yMjLIyMjAu5JVf1uaOzevG773DOpap8HI568cwcTcFisLm4YMzSh+OXGaB7s3zsyvIq0GC7Uaa+u6tcyFhYUxbdo0IiIi8PDw4NixY9WunQOwZMkS3njjDTp06ICTk5NhAC7Uvbhk9/6vcOVs7es8NQYpLxO5deMvPGohyvNXquT1sKSicXNUqxaWb7/9tsr9zz33HM8991yVx0RGRpb59//+9796zQpoCs1pDEtqQjwrXnoWSa+jbXB3XH1r1krg4NGWIXMeo8vwUWz64lMc3dxw8my6AlvNSckTsiUtq16VlIt/FSO8eeoIt69ewtGrA4FDRtR4AUhNRhJdek5qrBCbzOXkZOzMVFibN04dlsTka2jSsxg1pG7FzkoWWyytJmvn+Pr6VtqdVpfiklCypIDxx61pb15FZts0H57M1SULIGZh59p8x2oYg42NDfb29sTFxRkGwzc3xv9rbQGMPa058/YtTu+IwNLWnsgfvgagz6Tp+NZhJoi9mztz/vVuQ4fYoly/fh0XFxcsLBq/uFhT6DfrUZx9/Lm4bwcFWRlkpCQSG7mLm9FHmLDo3zW6hlTNauQtQUJaOpFxN/lbaN1quNSEg4MLDp7ebN79I8P6TcHMtGX/DZmY2ZJ8/jdcO000yv11txLRXTiC6cimqXZr/mcrjlhPqGLe3t6tp4XlfiRJEuj1yIw4rTnyx28N3TgAzn5B9JsxWwwaqwNJkoiLi2sxq5PWhFyhwC90AH6hAwzbzu/fQ8TSD7gRE41XcNdqryFJta/a2tzsuXyVx/r0atR7WFvY8sTj7/DVd68j9W1e04PronPvZzhz4H1qP0S7fiS9nqKjO0FT2GTJCpRKWESXUIXatWvHyZMnycnJwcqq+Y3xafkfqxpZyaJ7xhh0m3ErhSMb1pRJVgbMfozZ/1oskpU6Sk9PJzMzs9WMX6mMf79BePr7EfHx4mpXr5b0eiRa/psvktQkz9P0rLvIZHLMzZrnwMS6uHF5S/UHNQB9Tga5678g/eN/oVM6oho4rvqTGpBcocDU3FIsgFiJ5j6ORbSwVEOvK166vim7hLLu3ObAutWc27PdsK3/zLl0HjQMqyYoX92axcXFIZPJWs34lcpISAQOH0vE0g+4duIInf5cr6kily8fom27ZjTIvQ5yc3MwM2uaukxQXLQxNeM2DrbOTXbPxhLY7yVi9y2myLMfJqYNP/BV0uSTt2k5+vwC5ObmmA+fgaXajtSfNoGlLeadmnbhUXNLsQBiZaytrXFwcOD69esEBgYaO5xyRMJSjZIWFlkTJSx6nY4tX39GwuniAXYqSyuUlmps27iIZKUBxMXF4eLigrm5ubFDaVSHfvmBw7+tw0Rlgtq56toW2ZlJdPSrW2Xc5mLPlesM7tA0g8h/XvkenfoMaFWtnL59nuXCqe9AW4BX0AOobRomoc/b+jO620lYjH8MhW3Z1y+Hh8aSGRFF3tFYrIb0xtSzYTum9PmFZPy2C6WzA9ZDe5Oz5wgFl29iqjQV6wlVoV27dly/fr36A41AJCzV0Ov/bGFp5DEsuRnppKcksefnFdy+fIFx81/Bt1dIs5md1BpIksT169ebVVHCxmKuLp52O+W1/+DuV/l4nZu34rCzaxlrKVVGr9NxPS2dsV3K/l6LtBoS7sThYuOGuUXD9cc7u3kxZeyTdT5/06ZNLFiwgNy8PKbMnMWoMWOLd8ggLzePl559trizXoJbKck89MgjTJw2nZeff470tDRM/py2vvTPWZtJ8Tf4+pOl5OXlERQUxIoVKzAxqd3Cl2am1gSFPI9er+Pi6RUkI8Ov2yN1foySXk/2iiWYh47EYtSDlR5nE94fvVZHTuRxcg6cojgFlAESUn4hlv27kXf8LJJMBgWFWA7oSd6xM1gN6IbKo2wiXlIMTq/RkPl7JPrcfOxmhFFwMY7Ub9dj1b8nlgN6YrboELl3UiqIRoDibqHjx4+TlZWFWt34U81rQyQs1TB0CTVC4lBUUMDR33/l4tHDpCfcMGwf8vDjdOzTNMut30/S0tLIzs5u9eNXALqOGsfJTWs5vWVDpQmLpNdz52oUPfo+1MTRNazfY88yvVsXADKzUok4uQ4TmRK5XIGrnQcxccfIycvCRKnCXGlBhiaDsO4Tcbb56xO9Xq9n6/H15BVkUagpoG/QCG4mX8bTsQNerj5luoS7dx3Mxx8/z7N//xBFLT/IaLVa5s+fz549e9h18hBvz1/EG//3Mvb2f7U+TLlavCyJJEl4e3vzf/Pn065dO5bY2LDyxx/LNdX7+vnx0w8/0KdPH5YsWcL333/P448/XuufIxR/MLOx70hW6qU6nQ+gz8kk+4f/YjX9KRSO1SfDcqUC9fCKZ3ZlbjmA9fBQTJzt0ev05Ow9ju3EoWTvPkrmpt3IbWxBU4RUVITMwhyKtKBQYDNmEEq74kJ6Fl39sej6V3E86w5e3Dp/tsL7CWXHsTS3lexFwlKNxuwSuh13jcPri2soePXsS6+wcEzMzHDx8W3wewnF05llMhmenp7GDqVR6XU6jv2xlpzMLOzbVj4+IPZ0BO2DwpswsoYXeeoap0+eRZ91EnMTczILM5k64BFMFBW3MGTnZyHT6tkbu5WU7GRUShXmJhbo9FqGBIXj7OCOJElEntiEvY0zN1OvcfDibkyUKjLy07A1t+dWZhJZ5JJ0K462rh1qFe/Ro0fp3Lkz7u7uOF1tQ3h4ONu2bWPmzJnljj106BAuLi60a1d1V9ftW7fo06f4DX/o0KG8/vrrdU5YANJvnaat75jqD6yANuEyeZt/Rv3Yq8hM67/4pM3ov7oq5Qo56qHFY61sxgyo7JRqWdjaUZBf9UD0+5mVlRVOTk4iYWmJMu8Ul8A3beCaHTnpaZyP2gPAuPmL6ry6rlBzcXFxuLm5YdaIq/g2BxcO7iNq9Y94BQbTY1zFxeASb11FZWGDnXXLGxel1+vZf+Y2F89ex1xVyD8fmUGRTkN2biaO1RQgszYvbuIe0/eBSo+RyWQM6Vl+9kpa9l3srR0BeO/oezg61b4rLSkpqczichWtvFyiogUNZ82ahVKpZN68eTz99NMAuLq7s23bNsLCwtiwYUOl16upgJ5PEb3nn3QZ8matCssVntxL0cVo1E+8Wa/7NzYLtQ35ImGpkre3d8Ov79UAxACJahQVFC8GZ+3g1GDXzElPY/17b3N6R/FUQptqBkUK9VdSf+V+6A5q094HS7UVN8/FsvmDt7kefcIwFgtAq9Fw5eguOng3bs2Shlak0xNxNJENUfF4u1ozMKQNHX3tkMvlmJqYVZus1FdJsgLgaunKN7HfcObOmVpdoyYrL5cct379eqZN+2tF5ZUrVxITE8POnTtZvnw5e/fuBeAfL/8f77//Pr169UKhUKCs56rrMrkch/ZDuZ1wpMbn5O9Yjf5OElYzn6/XvZuCubUabVERRZrC6g++T7Vr185QAqI5ES0s1dBrtUDDTmte9e9/kn0riSGPPEnQkOGYNEDTqVC1u3fvkpOTc18kLA7ubZn32Q/E7t7OmR1/sH7xG8VvZCZK7JzsyUzLID83n+jfIukzbiLdRo5t8BbEhpSdX8TOk8lIksSQri7YWf05fdmpPZv3RXI7NQ1nh7q1FJUMgNXr9bz88svMmzev3DF6vZ6QkBA8PT1Zu3YtANsWbyM2NpZ3895lQP8BfPfld2RoMlDKldiZ2lXaJeXu7v5XC4gECQkJhu6c0qKiovD09KRt27aGbW5uxS069vb2TJkyhWPHjjFo0CA8vb3ZuXOn4bwLFy7U6WdRmqt7Hy4e/wIXz+pbfnNWf4JJxyBMu1c+db45KSnPn5+VhYljw30QbU1Kyj7ExcXRpUsXI0fzF5GwVEOnK05YKlpgsKb0Oh1Jl87j5NUOrUZDVkIcQx5+gu6jxjZUmEI1bty4cV+MXymhVKnoNmosXcPGkHjhLLfjrqHJyyMjJRG3zpa07xHCucNRHPx1JYd/W4tr52A82/vgFdQFK3tHLNQ2FBUWkHz5IunJiXQM6Y+1g2P1N25AyRn5HIq9jUolJ7y3O6Ym5T80jOo/gF82bmXciEFY13ImUOkBsGq1mu7duzN58uQyA2CheA21du3aodP91Ur1+eefo1ar0el1hIaH8sT/niBgSAB6vR4rEyuUciU+dj6EeYeVuVbv3r05c+YMiYmJ5OXlERERwT//+c9ysd3bHaTVasnIyMDR0ZGCggK2bdvGSy+9BEBmRobhmCVLllS7nltNmKgs0ek16LQaFMqKF9OUijRkf/8fLEbNROnZclY+tzAkLJmoRcJSIUtLS5ydnblx44ZIWFoSvbZuheMkSSLl6iX2rP6ZWxfPotcUIjcxRV9UiFxlik+vxlvvRCjvzp07ODg4YGradMXFmgOZTIZHp0A8OpUvAuUV3JX+Ux/g+JaN3Lh8icMb13NwzU8VXufciWPM/uc7jR0uABcSMzl7LQMbaxUT+7WtcoaeQq5g6uiRbN0bhbOzPSG1eHEtPQAWqHAAbFpaGqtXr+aVV15h2bJlhu0l0z0lvYSLyoUpflOY2HuiYX/snVjWXlpLJ/tOeKr/SpKVSiUffPABQ4YMISc3jzff+CcODg6Eh4fzzTff4Obmhl6vZ8OGDRw7dsxwXmFhIWFhYRQVFaHT6Zg+fTqjR48GYNe2rbzx0otIksS8efMYMWJEjX8GFbl5eSuZd89hqrKpNFnRpaWQs/ozrOcsICIyigUjxlfaSnX06FEeeeQRCgsLmTNnjiFBmzVrFidOnMDExIRx48axePFiAB5++GH27dtn+BlHREQYWpcagijPXzMuLi7cuXPH2GGUIRKWapS0sMgVtftRxe7axo6vl4JMRp+J02jT3ofUhHgy797Fp2dv1I4tv0JmS5KamoqDg4Oxw2h2rO0dGfJgcb0NvU7HrWtXKMjJJj87C5lcjquvP0c3beDSyWOGOheN5djlu8Sn5OLexpIpA2peuEylMmH8iCGcvniBXQcPMaxvaI3Oq8kA2FdffZXXX3+9wvOnTp3Knj17CAsLY/z48WX2BTkF4WjuyP9O/o/3Br5XZt/48eMZP348W/btZ/TA4tkupVdslsvlJCQklDnH0tKy0tWaJ894gK8/+6yaR1szF6NXYG7tRlDo/EqPKbp0ivwDW1E/+RY6vb7aVqpnnnmGVatWERAQQGhoKJMnTyYwMJA5c+awcuVKtFotI0aMYPfu3QwdOhSATz75hLFjG6cF2lwtFkCsCQcHh2Y38FYMuq2GoQ6LsvoWFkmSuB13jXP797Dr+y8BmP76O/R/YA6+vfsSMnkGYY8/Q4fuLWuwY2sgEpbqyRUKXH39aNetJwEDh9Kp/2Bs27jg26M3hXdvs+WrpYZp/g1Fr9ezKzqZdftvYGlmwuQBXvTpWLeupy5+/mRk5qCvYYzVDYA9deoU6enpDB48uMLz165dS3Jy8diaXbt2ldu/N2Evf+/690rvr9c1v/Wbsm8ewNWz8hpQhUe3o4k9gvqRRcjk8jKtVNbW1oZWqhJJSUlotVqCg4NRKpXMmjWLjRs3AjBq1CiguNUpKCio3rObasrE1Ayl0oS8LJGwVMXBwYG8vDzyqlmLrCmJhKUaJYNuFdW0sGSkJLP636/z48vPsWXpB1i7ePDQux/TtnPzmsd+Pyrp/xcJS92069qDkU8+z/k929n0xceGmXP1UVikY9ORBH47EI9/WxumDPAioK1Nva/byb8dG3dFciczrdpjywyApXgArKvrX8XkDh8+zP79+/H29uaBBx5gy5Yt5eqbqFQqJk6cyG+//VZmu06vIyE7gbbqtlTGzd2Bjbv2cOT06Ro+usYXOPJ9Lhz/kjOHPqSoMKfMvvydv6DLSMdyyl9VfqtrpapJK1ZWVhabN28ukxi++OKLdOnShTfeeKPCxLK+zMzNycvKaPDrtiaOjsUfHFJTU40cyV9El1A1CvNygYo/jZXIunObHxa9QFFeLiMef5aOIf0xs2x+S3Pfr9LT05EkSSQs9RA0ZARyhYKtyz7mypGD+Ib0I7DvQNz8OqEyq/m6TBk5heyOTkEulzG0mytq89qVkK9OQDsf3B3bcPL8OXJz85HJQAI83V0I9vUvc2zpAbBqtbrcANinnnqKp556CoDIyEiWLl3KV199hVarJSEhAW9vb3Q6HZs3b6ZXr7KtpkX6IvSSnj3xexjsMbjCrrRuHTvTrWNnriXe5I9dezBVmTA4pBemJrUcZ9WA7+dmZjYEhc5HU5jNhZNfAeDX/W8Ubf4Jhbsv5n2Gl711Na1UNdn/8MMP8/TTTxtmRC1evBgXFxfy8/N54IEH+OGHH5g7d261sRfGp5B3+CLIwSzQG3O/yrsVza2syUu9W+0172cl3XqpqallZqsZk0hYqmFmVVzeubKE5c6N6/zw8nMgSTz4zoe4+HRsyvCEGrh7t/iFSSQs9dN54FA8/DsTs2cHp/fu4mLkTpDLceoYwLgn/46dq3ul5968k8Ox86lYmCsZG9IWlbLxGndtrK0Z0rvsoPbTly7w66btjBrSD2tLS6DsAFi9Xs/ChQvLDYCtiE6nY+bMmeTk5CBJEgMHDuTJJ8uuLWSmNGNh74VEJUbx/rH3GeY1jO7O3StMXNq7e9Le3ZPsvBx2HjiMTqend3AgLk7Gm8GiMrUmKHQ+RZpcDn32BCGjF6Dy71buuIpaqUpP066uFWvhwoXY29uzYMECw7aS/RYWFjz44IPs37+/0oRFr9Nzd9lmTNrYoMvV4DBnGFKRlqxtx8mPvo5FTx/MOngYjpckCX1BIeaW1uQ1o5aD5kilUqFWq5tVC4tMaoz2NiPIysrCxsaGzMzMBl2w6dy+3Wz57EOe/3E9StVfI+YlSWLLso85v7e4/sHsJZ/g7N20y6QLNRMVFcW+fftYtGhRq1ph15gkSSItMZ6E82c4uHEDBZkZDJvzGEFDw8r8jKOvp3E1PhsHezMGBjg1yppcNaXValn5ewRdgn0JaOeLST0LrNVUgbaAledXcjf/Ls91fw4zZdV1l/R6PVEnT5CRmYOXqzNdAjpXeXzEvn2EDxzYkCEDxb/jQz9/iM+Q8Ti7V7xciFarpVOnTkRGRhoG3R4+fLjMh4OePXvy3XffERAQQN++ffn2228JCgriiy++YN26dURERJRZrPHKxkjaWKopuJ3OM58spndAEI+OmAB6PXJzU/SaouJFYf/8O7Pq3xmFk225vy1JksjeFUvR7dvIZApQypDJZMhMTIg8+Du5ukIe/O9HDf5za01WrFiBubk506dPb9T71PT9W7SwVKeSN7hLh6M4v3cnJmZmzH1/qahW24ylpqbi6OgokpUGJJPJcPDwxMHDE/9+g9n+/Zfs+Gop0fv2MP3lfxJ1OYusLA0d2qqZMrDmM34ak1Kp5IEJo4mLT2T9lp1MCx+JXNH4CZSZ0oxHgx4luzCbz6I/w8rEiok+E2ljWbYyb1zcXhJun0YuVxLSeRbp2YmcOhaB5aVrdBg9HJlpzbveGsKRXz6h/bAJOLv6VHpMTVqpli5dysyZMykoKGD27NmG1dKfffZZ2rVrZ+hOe/7555nWsRePvbGQtPxc9OgZPHQoL3z8vqF6ry47D4V1zYocymQy1MMrHkNok3GWtKPHKtwn/MXBwYH4+Hhjh2EgEpZqlLzFSX92FGfdvcO+lcu5eGAvcqUJ8z75BgsbW6PFJ1RPzBBqXKYWFox75h9s1BRy6XAU6/84zJCRobjaN7/quSqlCR3beePsZMdv23czefTw6k9qINam1izouQCNVsPKCytJK0jjycB5nIxZTmFRLp7OXenf+zmKNHkcPv0tpgpTRo15iaJ8DZc3baV92CCUVhUMTC6q/yDoex1dvwzPviNwqSJZKVEyTbu00tO0Q0JCOHu2/OrI2j8nNABk7zlJUXIGMpWCvSePVnqvmiYr1bFQ25CfJ2YJVcfBwYHo6Gj0er1RW0dLiISlOn9+Kt/1zeeci9qLpNOCTEbX8IkMfOAhUVa/BUhNTaV9e9Fd15iOb9rApcNR+PUdRvj0wcjlNS+0OGnSJCIjIxk2bJih9H1pQ4YMIS0tDa1Wy4wZMwwDYwcMGEB2djYAiYmJPPjgg3z00Uc1Kjxma2VDUJAPv27ZSkjXrrR1bboWUpVSxcOBD5OcncDfNk7j+7GrUZnbGfabqCwY0Ouv6dAm5ip8Jozh6uYdOPi3x97vr3Fykl6PQ9Iljp+Kp2e3xxokvrMH/sDBNxA3z4AGuV5VCq4mknvoPJYh/lgP6d7o9ythrlZTkJ+LXq+r1d/q/cbBwQGtVktWVha2trbGDkckLNVx9u6AhZMLZ/fuQm5iwpC5TxIwYAimFpbGDk2ogfz8fHJzc0ULSyM7GbEZv9CBjH3+H7U+97nnnuPRRx9lxYoVFe7//fffi8vg63T079+fcePG0a1bN/bv3284pn///kycONHw75oUHvP18MbH3YsNO3Y2acJS4uSxz1k+fi0mptbVHitXKvCdMIrMM8eJ27odAFNrS3LvptNl1HQS8s4TefB9nO198fcdW+tClyW0mkIyEq7Rb8YLdTq/NvSFReQePI/D7KZr5SphrrYBSaIgJ8dQql8or/TUZpGwtAAOHm15auk3ZN5OwdLWvszAW6H5KxnhLhKWhpeXmcH16BNcO3mC7NQUXH3H1ek6Q4YMITIystL9JS0lGo0GjUZTbixSYmIi169fZ2AdBp7KZDKKCnQkp93G1b7pqk+fOr2CDu4hNUpWSrMJ7IlNYHHLSuHdO7j2Kx4H44MzPl6DuHP7LAdOfI4kSZiamNHFfxpmFjVfGDJm+0qCR8+uVUx1lbn5CDYTQprkXveysP5rPSGRsFTOxsYGuVxOamoqHTp0MHY4ImGpKTGotmUSCUvjuBEbzYYl/0JXpMHOzZs+E6fTZcToRrtf3759iY2N5emnn6Zr165l9v36669MmTKlTB/7iy++yKuvvsrEiRN58803qxxwbWljyrX4+EZPWJKST3IlPorcwiw8bTvg7ze++pMqIZPLMXNuU267k3NnnJyLZxUV5KcTc2EdBZriWlIB7Ybh6Fx+TanSCvNysFY3zXPFelhXsrYcBwksenTAzLfpan2UlOfPy8pEvDJUTqFQYG9v32ymNouERWjVUlNTsba2vu8WPWxMBTk5/PHBu7h08Gf8gpeb5BPqwYMHyc7OZurUqZw5c4bAwL/eeNesWcP7779v+HdtC4/1Dgpm+Rdb6ezrg61F4zyW87GryNFrGNi7/isp15SZuR29uxUvRKjX67hwZTPnrheXYWhj246OPmORlVrUNe3WDSydmu6DmdLGCvsZgwFI/WFnEycsf7awiPWEquXg4CASFkFoCmKGUMM7E7kDraaAcf94qUmb062trRk6dChbtmwxJCzx8fEkJCTQt+9f69/UpvAYgLO9A/OeGcup0xdJy8wguFNHfL28Gyzuw6e+oY1DRzp5NnytlJqSyxUEdPyrRSf5diz7jy8t3ocMUwt7cs7cZMCUBZVdolEUXLxB3omrmHWqvOhgYzCzsEQml4sVm2vAwcGBc+fOGTsMQCQsQiuXmppaZi0ToX4kSSJ29y7c/IKwtLWr/oR6ysrKoqCgAGdnZwoLC9m+fTsvvPCCYf+aNWuYNm1amS6f5ORkXF1d0el0bNq0qUwyUxl7GxuGhPYm6tRxLl6Nw9nBAWtLS+Sy+k3lvHj+N+ysPWhnxGSlIq7OQbg6F9dDQZLIz07m1pFPUaqapiUya/dJdKnZKF3ssJ81tEnuWZpMLsfM3Iq8TJGwVMfBwYGMjAyKiorKFPgzBuNPrBaERpSTk4O1de0GNgqV0xZpSEuMo2Of3g12zbCwMKZNm0ZERAQeHh4cO3aM8PBwkpKSyMzMJDw8nODgYHr06GGYJVRizZo15apwPvjggwQHBxMcHIxareZvf/tbjWPp360nowb2Z+eBQ/y6sXg2TlxKQp0eV15hNrcyr+PnM6pO5zcZmQxztRuO1q7VH9tQt5TLMHF3wHpA0y8Oq8nWkHw0GaXcnNwMkbBUp2TQe3NYtVm0sAitWmFhoRi/0oBMVKaYW9uRmli3N/GKbNu2rdy20oXHjh8/Xum5R44cKbdt9+7d9YpHqVTi1saZW3fv8sfO3VhbWnHs5FkG9OtGYa6WfVsiGDk5nDZ2Fa81VOLU+bWEdppar1iaiqTVVlrVu0HvI0lk7z6BNjUH804e1Z/QQDKuZ5J+NhW5SoHSTIFdRzss2ziQk57eZDG0VKo/Z8ZqNBojRyISFqEV0+v1FBUVGZ5wQsMIHDqcY7//yq1rcfiFhtCxTz/UTk03JbgphN4zE6lIo2XXtn1kXEtk4LDB7NmzhvGj52Fh/teq7LqiQg6c+gK5JEOSy7C3csPEpnmsclstbT4oGjex12u1pK/ahWWfQNTDmqabVpOtITkqERMbU9qNLVs80tLGhlzRJVStkg98hYWFRo5EJCxCK1byiUC0sDSsftNnYePkxMVDh9i/cgX7fvqerqPGM2TuY612vSYTlRJXcwt6TBqBk6cLrn5PsXnHDyhVKlRyE/r2Gs3xM98SGjQHM6uWl7zJVBagrfsb0qZNm1iwYAF6vZ6XX36ZefPmGfZJksTBzTt47Im/oTVVMOfhhw3VigsKCnjyySc5dOgQcrmcr7/+mv79+9eoWnFV9Ho98dtugFKOx3BPFCblq9la2tmSmpBYwdlCaaKFRRCaQMkTTLSwNCyF0oQuI8LpMiIcTUE+JyP+4MAvP2Jla0vviS2jC6S2dFotmXfS6TK8uNCZysSUSeHFY2NyC3PY/sMrdB8xvUUmKwDIFSBJdTpVq9Uyf/589uzZY1ixedKkSZjEJqC9lY5MIefvr77A6s2/ExgYSGhoKJMnTyYwMJB///vfdOzYkeXLl1NUVERubq7hujWpVlyZ5P2JOPVywcKx8gUjLWxsKczLrtP17yfNKWERg26FVkskLI1PZWZOyOQZdBs1nqhffiQ9JcnYITWKI79H0iN8QIX7LE2tGDfrbe5sXNfEUTUPR48epXPnzri7u2Ntbc3oESNZ98r7qNztsZ8xmPy+PkgWpnTt2hWlUsmsWbPYuHEjAD/99BPz588HwMTEpEHKv+fdzUebp6syWQEwt1ZTmJeNVMdE7X7RnLqERMIitFolTzDRJdT4+k6bhYmZOdu//Iz4c7Fk3b3Tat4ILh09i7O3O5Y2VpUeIyFh7+XbhFE1giq68wquJKLLzq1wX1JSkqF0QFHyXezvFpLZzs5QCK70fgAPDw8SExPJyMhAqVTy4osv0r17dx555BHDYpZQXK24S5cuvPHGG7X6W8q8mE6b0OoL4Fmo1eh1WjT5+TW+9v1IqVQik8maRQuL6BISWi3RwtJ0zKysGP30C0R8+iFr3loEgEyuwNm7Ay4+vljZ2WNhY4ulrR2mFhZo8vPR6/UoTUxw79QZkyaq/1FbmXcySEtMIWTSsCqPS9i7Dkyq/kTf/JVNCnQZeeQeu0hRyl1UXs7kRp3F4eGR5c/6M5nQZmSTtf0UFj07Ii9VQbeiZEMmk1FUVMTVq1cZPXo0S5cu5ZVXXuHdd9/lnXfeqXW14hJ6vZ6sG1k49Sq/bMG9Sle7NbWwqPb4+5VMJsPU1FQkLILQmETC0rR8eoXwzHc/k5GSTMatFJIuXeDqiePERZ+mICeTwrwc7n1TBJArlIROnYlnYDBt2vuiUDaPlyW9Xs/JLXsZ9FD1izp6j36Uu6d2cu2PZcjkCixdPHHuWfP6K1UNWs3OzmbAgL+6o65fv85bb73FCy+8UOmg1RdffJGNGzeiUqno1asXX331Fcpqfq6aO4Wkr9tvGMsiN1di0dMX9YhuAGTmHaHoTgYmTrZlznN3dychIYGM9QexnzOcxAWb6NOnT5n9iYl/DW5NSEjA1dUVR0dH1Go1Y8aMAWDSpEm8+eabQO2rFQNkXs8kNfoObcO8UarKD7K9l7l18YDe/KxMbNuIteKqolKpmkWXUPN4ZRCERiC6hJqeQmmCg4cnDh6edOjRmwEz5xj26XU6cjPT0eTlY2phgUwuJ+vubU5s3sjhdb9w4JcfUVlYETplBt1GjUWhNG5VzSO/7aFn+KAyiypWRiaT4dR9BE7dRwCQuG8dtw6sxyF4EErrqpeGqGjQ6uTJk7G3L15l2dramujoaKC4tcLb25sJEyYAVDpoNSwsjCVLliCXy5kzZw4//PADjz76aJVxqDw6YzG+X6WPV5eei8JBXW577969iT16gpx/eWCan0dERIRhFhCAm5sbCoWCmJgYAgICWLVqFd9++y0ymYyRI0dy6NAhQkNDiYyMpFOnTkDtqxXn3Moj42I67Sf5VHlcaSXLSuSJ8vzVUqlUzaKFpVZjWJYtW2aoHqlWqwkNDWXLli2G/bdu3eLhhx/Gzc0NCwsLRo0axeXLl6u97rp16wgICMDU1JSAgAA2bNhQ+0ciCPfQaDTIZDKjl5MWiskVCqztHXHwaIuVvQOWtna4+vgx9vkXeebblcx65wN8e/dl70/f8fUzfyN622ZyM4xT2OvysXM4erpg7Whbp/PdB05BrzDl5t71nF3+BikH/6DgbsVTaO8dtBoeHl5hMT2AQ4cO4eLiQrt27YDKB62OGDEChUKBTCajW7duZVo4KqK7nYqqrW3VyZlCXuF+pVLJvx58kpFTJ9GtWzdeeuklHBwcDNWKAZYuXcrMmTPx8/MjPDycoKDiZQGWLFnCiy++SHBwMPv27eOVV14Bal+tOC36Nm16V98NVFrJis1iAcTqmZqatrwWFg8PD9599118fIqz2BUrVjBhwgROnTpFQEAAEydOxMTEhN9//x21Ws2HH37I8OHDOXfuHJaWlhVe89ChQ8yYMYO3336bSZMmsWHDBqZPn05UVFSZZkVBqC2NRoNKpWq1tUFaExMzM1x9/HD18aPXuInsX/UTu777gl3ffUGbDn6EP/sP7N2apthY5p0MUhOSqx23Uh3XkDGG7zMuHuH8hi8IfmghCvOyS0VUNii1ImvWrGHGjBnF1yw1aPXgwYN06dKFTz75pMxSFFqtlp9//pnPP/+8yliLzsdiGlL1662qrRP55+IwD/Aut2/ywzMZN3oM6qFdDdtKVysOCQnh7Nmz5c5r3749Bw4cKLe9NtWKiwq1aHI0mNnXbgyRQmmC0tRcLIBYAy2yhWXcuHGEh4fTsWNHOnbsyDvvvIOVlRWHDx/m8uXLHD58mGXLltGrVy/8/Pz4/PPPycnJYdWqVZVe86OPPmLEiBEsWrQIf39/Fi1axLBhw/joo4/q+9iE+1xhYaEYv9ICOXh4MvGlV3jyyx8Y8fiz3Lp6gVNbNjbJvUvGrfSeMKRBr2vr14fghxYSv3t1uX2VDUqt6Lj169czbdo0gDKDVk+ePImrqyvvvvtumXNefPFF+vbtW+2HP0mrRWZa9Ru+VWhn8mPiKtxn3rk92rsZaLMqnknUWDJvZnNjw1W8R7er0/mmFtai2m0NtMiEpTSdTsfq1avJzc0lNDTU0FxkZmZmOEahUKBSqYiKiqr0OocOHWLkyLIjz8PCwjh48GBdQxME4K8WFqFlsrS1Iy0pEZlMToeeDbfYYlWO/L6HHjUct1JbCnNrJEnHzd2rkfR6w/bKBqXeKyoqCk9PT9q2LZ4uXNGg1ZKxLgCfffYZFy5c4H//+1+VcUmFhcgUNWuF1OcWkLntOPpS8ZewGRdC9s5TNbpOQ0g5nsKdqETcR3mhtKhbt6+5tZrc9IyGDawVai5dQrV+VsbGxmJlZYWpqSlPPvkkGzZsICAgAH9/f7y8vFi0aBHp6eloNBreffddUlJSSE5OrvR6KSkptGlTtu+xTZs2pKSkVBlHYWEhWVlZZb4EoTSNRiMG3LZwN2PPYOvqgVdQ1wa75q1rVziyYQ1FmrIvwJeOnsWprRvqOo5bqYl2Y5/E0saW2B/eoSi7eHxO7969OXPmDImJiWRnZxMREUFYWFi5c0t3BwFlBq0CZQatbt68mW+//ZY1a9ZUOzuo8GAUqh49ahS/42OjMA9oS8YvkWT8ftBQm0VfpCVr63GUjk2zMnri4WT0+Tp8ZvljbmtW/QmVsFDbkJuR0XCBtVIttoXFz8+P6OhoDh8+zFNPPcXcuXM5d+4cJiYmrFu3jkuXLmFvb4+FhQWRkZGMHj0ahaLqKWb3Nn9KklTtuIPFixdjY2Nj+Cr51CEIJUSXUMvXZ9Jk0pNucuHA3noXopMkidPbI1j52otErf6BH1/+B2lJpVo2zl/BO7hDfUOulkOPUXSe8Q/iNn1O1qUjKJVKPvjgA4YMGVLpoFW9Xs+GDRuYOrXs0geVDVp9/vnnSU1NZeDAgXTt2pV33nmnwli01y4js7JCbm1b4/hVbdtgP3Mo1kO7kL0nhvR1+8n84xBWA4OwHtilbj+UWojfeRMzKxPcBtR/TJOVnS15okuoWgUFBdU2IjSFWk9rVqlUhkG3PXv25NixY3z88cd8+eWX9OjRg+joaDIzM9FoNDg5OdGnTx969uxZ6fVcXFzK/SBu375drtXlXosWLTKMjgfIysoSSYtQhlarFTOEWji/0P6c3x9KxNIP2P39N4T//R+06eBrmJJaU0UFBWz94lMuHdpL0NBRBA8PY+P/3uPHl58n7Knn8AsdgEympYKejkahMLfCd+arnF3xLzp37MP48eMZP358mWNKD1qVy+UkJCSUu05lg1avXLlSbQySVofmwmUswsPr8AhAYW2J7fjQOp1bV8mnbmNiocQh0LFBrmdha0tBrlhPqDrnzp0zdghAA9RhkSSpXN+WjU3xi8nly5c5fvw4b7/9dqXnh4aGsmPHDv7xj38Ytm3fvr3aefempqaiuV+okomJCQUFBcYOQ6in8Qv+jxObf+fg2pWsf/dNACxsHUEvUaTJx9TCGlNLKxw92hI6dQYOHmU/uKQmxPPbe++QnXab0c8uIGBA8YDaue9/zNZln7L54/c49scW+s14BJVZ07XISXo9khFnsBXsjcR8YH+j3b8uci+m4T2+5rVWqmOhVosFEGsgKCiI06dPGzuM2iUsr7zyCqNHj6Zt27ZkZ2ezevVqIiMj2bp1KwC//vorTk5OeHp6Ehsby/PPP8/EiRPLDKqdM2cO7u7uLF68GChuuhw4cCBLlixhwoQJ/P777+zcubPKgbqCUBPNpTqjUD9yhYJe4yfTY+wEUuNvcjvuGrfjrmFiaoqJmTm56WnkZWaTcP4MK187zqx/v4eDhycA5/bvYfuXS7Gyd2L2ux+XSWZU5haM+8dCTm8PZM+Kr9j74/9wcH8DG+emqXoqk8uxcvFCk5aEyt6tSe5ZQpcUj9zKAplV+UJwzZoESouGq3dqrrZBW5iPtqgIpWiNrZSJiQkuLsavBlyr3/ytW7eYPXs2ycnJ2NjYEBwczNatWxkxori6Y3JyMvPnz+fWrVu4uroyZ84cXn/99TLXuHnzZpkR+H379mX16tW89tprvP7663To0IFffvlF1GAR6q25DBQTGoZcrsDJqx1OXu3oPKh8jZSC3Bx++r/5bPr4A2a9vYTdy7/hzJ6tdAwdxKgn/46JWfnBmTKZjK5hY3D19eO39//NihefZfTfF+Dbq2m6OhyDB5F+Loo2/ac3yf1KFJ6OxWJ03bqC6kOSJAq1OsxMap90pF1Ox8Su7gNsK1LStViQnYWVfdUVie9nhYWFzaJHQya1kiVVs7KysLGxITMzE7W6hX1qEBpFZGQkJ06cYMGCBcYORWgiSZcusPqfC1GamqEr0jD0kScIHj6qRsUDC3JziPj0A66fOka30eMY9NBjjb6ukV6n5frGL+gw8dlGvU9p2rhrSDlZmAR2bbJ7Aui0elb/cR5rMxNMlXLkChnIZMjlMny97fDwVFc5nfyLeR/xv33L0Evl11uC4orBjzzyCIWFhcyZM6fM8gAAU6dOJS4ujuPHjwOwcOFCNv3xOxkpyfTs25/Va9diIRZBrNCqVavQ6/U8+OCDjXL9mr5/i7WEhFZLdAndf9w6+jPllX9x9I8NDJw1mzbtaz7ewczSikkv/5MTmzew7+flJF+6wISXXsfKzr7R4pUrlKgsLLm59Rs8R82r/oQGUHTlKubDhjfJvUrbv/8GY4Z1wNambCtJkUbHpSup7NpzvczSmO4Olvj6OaAyN0Gr1bJk80dEHT9Q4XpLAM888wyrVq0iICCA0NBQJk+eTGBgIAA7duwoN1v1tdde45UF8/n2uXlctnDkyy+/LDOWUviLRqNpFsmcSFiEVqukS6gm0+SF1sMruCtewV3rdK5MJqPn2Mm4+Pjx+3/fYcWLzzDhxVfx6BTYsEGWkpuVg627V6NdvwxJAiRo4ueDJElkFWjLJSsAJioFnQOc6RzgXOb4+KRs9h9LQFuk59zZkzi5tqdAa4V7qfWWZs6cCRQvb6DVagkODgZg1qxZbNy4kcDAQIqKivjPf/7DJ598wiOPPGK4h1qtRmOiRJIkMjMyxGtEFQoLC7GzszN2GHWvdCsIzV1Jn2tRUZGRIxFaGg//zjz838+wdXFjzVuLOLZxfb3rwFTGf+rfKczNIi5yXaNcvzTd1RgUHu0b/T7l7quX0GprPmdcJpPh6a5m2MB2hA3rQFs3Ga7tPTl27jZQfr2lqtZj+vDDD5k7d26ZNZZKLFj4Mv/auIvLV6/yxBNP1PXhtXrNpWq4SFiEVqvkCSa6hYTamDRpEnZ2dsyd9zdm/us9uo0ez76fvmPdf/5JTnY24eHh+Pv7ExgYyKeffmo4b/Dgwfj7+9O1a1e6du1q2L5w4UKCg4Pp0qUL06ZNIy8vr9w9XXqNQptReUXwhlJ08w4mHRu/ON69lAo5MhkU5Nftw4MkSaiUctTWfw38LN0iUtl6TImJiWzfvp25c+dWeN1PP/2U/8yaSoe27qxeXX6dJ6GYSFgEoZGVPMHETCGhNp577jl++OEHoHhKdbdR4wC4EXOKvKxMXn75ZS5cuMCRI0f4/PPPyxRpW7t2LdHR0WXW9HnttdeIiYnh9OnTtGvXji+//LLcPQvvxmPlUHWxzIYgIUMmN07Xx7iRPuyOulmnc93d3bl9O5mcnOLn8r3rLVW2HlN0dDTnzp2jXbt29O/fn9jYWMLvKZRnbmVDv+Ag1q5dW6fY7gfNZZaQGMMitFolTzDRwiLUxpAhQ4iMjATg6okjbP7kfazsHZn08j9xdvfA2d0DAEtLS3x9fUlOTjZU/65IyawHSZLIycmpcKxE7q0bWLXr2uCPpbT6dGndvpXD8dMpmChk5BfqGDm0PWZmFb996LV6ft10ERtLE5DJ0Gh05Gm02Fub0sGzdhWKS/Tu3Zvz58/x3c4ThIU6ERERUWYWkJubGwqFgpiYGAICAli1ahXffvstQUFBhrXs4uLimDp1qqGC8OXLl/H19cVcrWbLgUP4d6vZekr3G0mSmk0Li0hYhFZLtLAIdaXX67h7M47f3nsb7y7dGfP8QswsrcocEx8fT0xMDN27dzdsmzVrFkqlknnz5vH0008btj/33HP8+uuv+Pn58cEHH5S5TmFaMlkJV3HtO7FRH5P2RjwmrrWb8ZSXX8TufXHYqU0ZNbw9crmcogIt67dcZvIYX0xUf72F6LR6cvOL2LLrGmFD22GrNjNslytk9RrUqlQq+ebLT5n3xHME/bqI1179P8N6S9988w1ubm4sXbqUmTNnUlBQwOzZswkKCqrymvPnz+f69evkpKXiamvLt/dMgxaKabVaJElqFi0sSK1EZmamBEiZmZnGDkVoJjIzM6U33nhDunjxorFDEVqQ3Ix06bXZ06UgDxfp8PpfJL1OV+6Y/Px8KTQ0VPr1118N2xITEyVJkqTU1FSpV69eUmRkZJlzdDqd9MILL0jfffddme0XfvmvpNdqG+GRlJW3Z4+k1xTU+PjDR+KliG2XpaLC8rHl52ukXzecM/w79VaO9O2q09La389L6al5DRJvZbZG3ZC2Rt1osOvt/PYL6atnHm+w67U22dnZ0htvvCGdP3++0e5R0/dvMYZFaLVEl5BQW0mXzrP8xWfIunMHj06B9Jk0Hdk9xcwkSWLu3LmEh4eXWT3Zza24vL69vT1Tpkzh2LFjZc6Ty+U88MADZcZKZF05ibV7B2TVrGjfIPR6ZCY1+5S8bedVXJwsGT3SB6WqfGympkoKCv4aQBt1PJFHZgQxZbw/tvbmDRZyRQo1Oi7EN9wKyxY2Yj2hqpS0UDeHLiGRsAitVslKzaJLSKiOJEmcjPiD1W+8jLWDI2FPPV/pitCLFi3CwsKC1157zbBNq9Vy9+5dAAoKCti2bRudO3cGisdKlNi4cSP+/v6Gf9+9cBy3fhMb4RGVJ5PVbAxL9Ikk2nnZ4tWu8robBw/GM3RwOwD0ej0qlaLJ6ph4uVqTnldEbm7DPK8t1DZo8nKQmmqp7ham5ANfc+gSEmNYhFZLLpdjYmIiEhahSpqCfLZ89iFXjh6ia9gYlqzewKlvV5Kbm4uHhwcbNmzgjTfe4JtvvkGv17NkyRICAgIMU5eXLFlC//79CQsLo6ioCJ1Ox/Tp0xk9ejTw11gJuVxO586d+eKLLwz3bm7FypKTsrmbXcjwHlUvxqhWm3L27G1iz9wmLauQUYO8myZAoIu/I21drPh52xX6BDjTxd+xXtczV9sgSXoKcnMwtxbLutyrObWwiIRFaNVMTU1Fl5BQqdTEeDa8+xa5GWmMeX4h/n0HMuzRp8odVzKzBCqfbXPixIkKt2/cuLHC7Zln95FzJ7HCfQ2tsphL6PV6Dh1OoKhIz/A/W05KbNq0iQULFqDX/7WGT1BQG4IonoZ99OhR+g/sVeM1fE6dOsWTTz5JXl4eQUFBrFixwtAaWlP2tmbMHO3D4hWn6dTBDpVJ3bvUSpKU/OwskbBUQCQsgtBErK2tycrKMnYYQjN04eA+ti37CEtbex5a/DEOHm2b9P4Jpw8T8MBLTXKvxLPnOXPHCsXuaxXuLyrS06NLG9q4lK0Gq9VqmT9/Pnv27GmwNXzmzZvH559/Tp8+fViyZAnff/89jz/+eK0eT1GRjr1Hk3CxNSe3QFuvhKWk6y8vKxN7N486X6e1yszMRCaTYWlpaexQRMIitG4ODg6kpqYaOwyhGZEkiT3Lv+LU1o349A5l9DPzUZk17kDRipiaW6Awa5o3gUspNxg1fXStzzt69CidO3c2lL1viDV8bt68SZ8+fQAYOnQor7/+eq0Slk1748jOLyIstC1jB5Vfm6i2zP9MWPKzGm4gb2uSmpqKra0tykZeubwmxKBboVUTCYtwr+vRxzm1dSN9p81i/PxXjJKsAMhNVNw+9FvT3KyOQ2WqWqOnuv2VreHToUMHtm3bBsCGDRvKXK86e48n4mhrxsxRvthXsJBiXZhZWQEy8rNFS2xFUlNTcXBwMHYYgEhYhFbOwcGBnJwcCgoKjB2K0EzkZWQA0GvCNKMNetUV5pGXkYpz6MRGv9e1a5dp5+5a/YEVqGjsS33X8Pnuu+94//336dWrFwqFotpP7tuPJrBm+1V+3HQJKwsTQrq41OGRVE4uV6AytyRfdB1XKDU1FUfH+g1sbijGb+MRhEZU8skgNTW1zCdB4f4lUfwmK1cY7/OawtQCZ//u3Nj5E17DHzJsL0qIo2DvemRyBchkqDoGoeo+pF73unH9CkOGjqrTuRWt0VPSnVPZ/nvX8NFqtdy5c4fw8HAiIiIICAhg586dAERFRXHhwoXKY0/I4mZiNvMmdapT/DVlamlNXmZGo96jJdLpdKSnp4sWFkFoCqUTFkEA0GmKkMnlyOVNUKytCs49w9AX5qPPSiX7+8Wk/efvaM8fwGr6M1jNfB6rB55Dc/lMw9ysji1JvXv35syZMyQmJpKdnU1ERARhYWGG/aXX8NFqtaxatYpx48YxZswYkpOTiYuLIyoqiqCgIMNMqzt37gDFA3qXLFlS4fiVs5fT+CniMleSsnl0gl+dYq8NcysbcjLEGJZ7ZWRkoNfrm03CIlpYhFbNzMwMKysrkbAIBlpNIQpl7abRNhZLFy8u/fIxLt3CsX8kpNx+qah+NYTSUu9gZ21V/YGVUCqVfPDBBwwZMgS9Xs/ChQvrvYbPDz/8wFdffYUkScybN48RI0aUO+ZachbhoR7Y2zXN+CJzGzW56RlNcq+WpOR1UyQsgtBExMBbobSC3Fy0muZRm8fOvx9Jxy+Tl6/BtoL9slrWJ7nXuTOn6BM6uF7XGD9+POPHjy+zrXRdmpCQEM6ePVvp+d7e3oYaLAALFixgwYIFlR4fdTKZ7Nwijp29Q1h/z3pEXnNWdrYkXbrUJPdqSe7evYuJiUm5gdPGIhIWodVzcHAwLDEvCKaWliibQREsgJzEBBw6dMRtwMAK9+vrOXOlSCdh0kwea01IksSFm5nMHuOLaT1qq9SWlZ0thbliPaF7paamYm9vj1zePEaPiIRFaPUcHBw4c+YMkiQ1u1LoQuMrzMvl4JqfsbSzx0Jtw9Hf16IwaR5v4rdPHMNjWPkukRJmfYaQs/pjkMlBktBl5aKwKdXFo9ciMzHDImwqMlNLUJpBM3lzqQuZTEaAtw1/7Ilj2sgOTXZfc2sbkbBUoDnNEAKRsAj3AQcHBzQaDdnZ2ajVovT2/eaHl54l6+4dw79V5hZM+r83jBjRX3IzMzC3r3x8gCqoH6qgflVeo+hyDLm7I6CoAHQaQKKk8Eqh3qsBo20afbu6cjUhi21RN+vUJaQtKiQ7IxkrW0dMTGo2fsdCrUan1VBUUICJWcPUd2kNUlNT8fRsmm65mhAJi9DqlZ4pJBKW+4skSTi160DW3TvM/e9nWNk7oDI3N/oMoRI2rm7k30rB0r3uJeFNfIMx8Q2ueOe63XW+rjF18FDz2944MnI0dO/kiK+XbYXHXYqOIi/vBvbOnSjIS0ZTWIBSaYGFlQu34q+gJx8ZIFeo8OsSVuE1oFS12+wskbD8qbCwkOzs7GYz4BZEwiLcB+zs7JDJZKSmptKuXbvqTxBaheunjrPpk/fQ5OUBcPnYYUInzzByVGXpNBoK09PrlbC0VgseLE7CVvxxEZ+2Nsjkf3XnagrziD26AmubDgSHzCQzNQE7RxesbSteZfrcyU2G728nXiM/NxWvjr0M20oWPczLykTt5NwYD6fFSUtLA5rPDCEQCYtwH1AqldjZ2YmZQveRpEvn+e2//8atYyd6T5iKha091y6eN3ZY5fhMmc6x/75Ln8CqpwLXR0sfuxXatQ2rtl2hSKfFxyoGlSyDvNwkevR7Hiub4kUY7Zyq7rYwMTEvTlokyMm+jIf3CK6e3UmHzsMBsZ5QRZrblGYQCYtwnxBTm+8vUWt+Ru3Uhimv/Avln1ODL54+ZeSoyjvy7tuo3dui0xY1Sm0YV0c1SQm3cW/bpkGvm5mbiY2lTYNeszIdPW3p4G7Nj/s20aX3GKwsal9XxjdoWLltyTcUxB75lY5dR2H+Z1exWE/oL3fv3sXCwgJzc+OstVURkbAI9wUHBwcuX75s7DCEJiJXKDCztjYkKwAOrm4kxV3Hzbtst2BBXh7H9+5BptcBoNfrQCZDhgwJMDU1xc7ZGRfv9tyKv0nStavIZDLkcjl6vR5HN3f8uvWo09TPPgtfJSlqHzciImg/fkK9HnNF/Ht0Yn/kqTolLHq9jj1nj5JbmIsMOSWDeXWSHkuVGbmF+UzsXfkMp4Z0JzsDP1fnOiUrlXH16oSTe0fOHvsFL59hKExMyRMtLAbNbYYQiIRFuE84Oztz5MgRCgoKMBOD6lo9z4AgDqz5iYLcHMwsi9/kOvXsTdSm38skLPs3/oZSpSK4Tyhqe/ty19Hr9RQUFJCanMSlUyewd3ZhwLiJZbpYEq9f48DmP5AkCd+u3XH1rPnMHLlSyZ0L5+j6xDP1eLSVM7WyRFtY+2q5qZlJRJw+yYjg3rjYVjym48zNC6w7vA3Vn4sXSpKEtbklff26Y2piWq+4S5MkiUMXjzKgU6/qD64lpVJBcMhMLpxej4mphegSKiUlJaVZzRACkbAI9wkvLy8kSeLmzZt07NjR2OEIjczOxQ29Vkt6ciKuPsVr0cjlcmQyGVptEUqlCYd3bsO7UwBtfSr/e5DL5VhYWGDRwYe2HXwqPMa9XXvc27VHr9dz/vhRLkefRKFU0qX/QKzU1XebKE1MGnmMSe2ufeP2DY5fu8JDA8ZUGVegpz+Bnv5ltqVlp7Elej86vZ5hgaHYWtavQqpWW8TaI1sJ6dgTR3XjfNqXyWR06jqFfWa/kZuZ3ij3aGlycnK4c+cOAwYMMHYoZYiERbgv2NvbY21tTVxcnEhYWrmc9DS2fPYhAIW5uWX2BfcbwKl9kWSlp9G+U+cqk5XaksvldO5dvB6QprCAU/siKczPx9TcguC+/TG3tKzwPIdOnTm9bClBTzzdSBVFpVodfeL6JaaE1K2rx97anom9hqMp0rDv3AHS8ooYHNAdZ5u6JRtrDm1jbI+hqC0q/tk1JHtXV/Iy7zb6fVqCuLg4gGY3q1IkLMJ9QSaT0a5dO65fv27sUIQGptVokCsUaIs0xO7axqH1v6BQmTL6mfl4d+le5lgbewduJ8QTOmoM9s4NOxC1NJWpGX1GjAIgLzub0wf2kZORzvDps8od69a3P+r2Hbi2fi0+U6c3eCzmpibkZOdhZW1R7bEZOeno9fp631NlomJ4lyEU6fTsiN5DeI/yg15rwsrMrEmSFQALG3vSkm82yb2au7i4OBwcHJrNGkIlRMIi3De8vb2JjY0lPz+/WY18F+omPSWJ7V8tJeFsDACyP1snfEP6M3Tu37C0tavwvDFzHm2yGAEsrK0JGTmag1s2odVqUSrLv+xaubiiKcgrt33Tpk0sWLAAvV7Pyy+/zLx588rsf+aZZ/j111/x9PQss8DgrFmzOHHiBCYmJgwbPBRTK0tCBne/9/JlbIs+gE5f1KADaU0UcrILNMTEnSPYOwCNVsfRy6ewNreki3enas/X6nQNFkt1LG0cSbpU+SKO95Pr16/j7e1t7DDKEQmLcN/w9vY2jGPx8/MzdjhCPRRpCvnlrVcAGD7vaeRKJTpNEe2792q2hb/sXVxJvHoFLz//cvsK09JQ3tOSoNVqmT9/Pnv27EGtVtO9e3cmT56MfanBwbNmzeLRRx/liSeeKHPunDlzWLlyJVqtlhEjRuDquKfKhGXD0X10aetMe9fysdXXjH6jOR13ng1HdmCikNGzQwC3M9PZdHwHY3tWnhztjo2iR/vODR5PZczVNhTk5DfZ/Zqr7OxsUlNTGTJkiLFDKUckLMJ9w87ODhsbG65fvy4SlhZM0uuJ/OFb8jPTmfvfz7F3czd2SDXi360HBzb/UWHCcmXjb/hOK1uF9+jRo3Tu3Bl39+LHFx4ezrZt25g5c6bhmH79+hnGG5Q2alRxd5RSqSQoKIjUtIrHZuj1etYc2kV/vw54OLav60OrVhfvTmVaVFzs3LC1smd11GamhY5CofhrqYSDF8+TnBaHj6s3Xs5NVwHYQm2DJq8AvU6HXNE8lm4whpK/p+bYwtJyl/UUhFqSyWR4e3tX+AIvtAx6nY5f//NPYnZsYcCDj1SZrGzatAk/Pz98fX355ptvyuzLy8sjPDwcf39/AgMD+fTTTw37/v3vf+Pp6VmuBoUkSbz00kt07NiRTp06sWbNmjrFXxGZXI7qnhaWpKQkQ7IC4OHhQWJiYq3ul5WVxebNm/Hp5MnG47vKfa09vJ0RQd0aNVmpjKejK6O79Wft4e3siN7L1ZQ4Vh3YjqNlAVNCR9eoy6ghlZTnL8i5v1dtvn79Ok5OTlhZNVzNm4YiWliE+4q3tzenT58mLy8PC4vqByEKzcudG9eJj40GwCuwS6XH1aQ75eWXX2bQoEHk5ubSs2dPRo8ejY+PD2FhYTz22GMEBZUtl//tt9+SlZXFpUuXkCSpTpWTczIzKtwu6XXlSuhLUvnZPbWZ/ixJEg8//DBPP/00YwLArdsg5Irm9ZJvY2nDjH6jycjNIe72Nab1GYRS2XA1XGqj9HpCFja2RomhOYiLi6NDhw7GDqNCooVFuK+UTNO7ceOGkSMRakqSJPb+/D1fP/846959y7D92sljlZ5TujvF2tra0J1SwsLCgkGDBgFgaWmJr68vycnJAPTq1QtXV9dy1/zyyy95/fXXgeLEoS5VQG2dnIna/AcF+WXHSrj0CiVh544y29zd3cu0qCQkJFQYV2UWLlyIvb09CxYswNq9C9nXjtQ63qZia2lF13bBRktWAC5nHwbu7/L8mZmZpKWlNcvuIBAtLMJ9xtbWFltbW+Li4ujUqWmbnIW6Ob75N47/sY5OA4dibe+AJj8flbk53cdUXsq+Nt0p8fHxxMTE0L171bNo4uPj+eabb9i0aROenp58/vnnuLi41OqxhI4aQ35uLpHr13Dr4jnuXjyLd5cejH1hIbfuScB69+7NmTNnSExMRK1WExERwT//+c8a3eeLL74gOjqaiIgIAKzb9SYp6mtsfPvVKt77RdTVn5FZFC/jcD8nLM15/ArUsoVl2bJlBAcHo1arUavVhIaGsmXLFsP+nJwcnn32WTw8PDA3N6dTp04sW7asymsuX74cmUxW7qugoKBuj0gQqiHqsbQckiRxcstGfEP6E/7MfAbMnMuwR59kwMy5mKgq/zRe0+6UgoICZsyYwX//+18sKynsViInJwcnJyeOHz9OWFgYCxYsqP0DAswtLRn14Fx6h4UDEHf6BEsfmcHObb+TmhhvOE6pVPLBBx8wZMgQunXrxksvvYSDgwPh4eEkJSUBMG/ePEJDQ4mJicHDw4MNGzYA8OyzzxIXF0evXr3o2rUrK374EaT611dpTW6knuK3sx+z8eynuNt1YWCnB0EmIz/r/k5YnJ2dq30uGEutWlg8PDx499138fEpLlG9YsUKJkyYwKlTp+jcuTP/+Mc/2LNnDz/99BPe3t5s376dp59+Gjc3NyZMqPzTkFqt5uLFi2W2ifVehMbi7e3NqVOnyM3NbbZPTKF4NtDqf71Czt3beIydWKtzK+pO6dOnT9nrSxJz584lPDycqVOn1uiaU6ZMAWDy5Ml88skntYrpXp36DcIvpD+RP37LqS1/UKTRsOHt15j3xQrDMePHj2f8+PFlzitpNQHKDSYuodVqy21LiPysXvG2NscSdzI1+KUy25SW5vf1ekLNfQZlrVpYxo0bR3h4OB07dqRjx4688847WFlZcfhwcd/foUOHmDt3LoMHD8bb25vHH3+cLl26lCloVBGZTIaLi0uZL0FoLCXNnWIcS/N24eA+ks6fIfy5l+g2alytzi3dnZKdnU1ERARhYWFljlm0aBEWFha89tprNbrm+PHj2bNnDwCRkZE17lJMSUnhzJkzXLp0iRs3bnDr1i10f84WkisU9H9gtuHYNg24VEA5MpBEKwsAKVkXcVWXH1hqYmlBblZG0wfUDGRkZJCRkdFsu4OgHoNudTodq1evJjc3l9DQUAD69+/PH3/8QWJiIpIksWfPHi5dulTuheJeOTk5eHl54eHhwdixYzl16lS19///9u47PKoqfeD4d2bSe4OEkBBIqKl0SAi9SVCaKIKCCop9dXdZf+quq67romtZxboKgihNQKR36QRBIAVCTyMkoYX0Mu3+/gjMEtNDkgnJ+3mePMnce+bOO7mZzDvnnvOekpIScnNzy3wJURPOzs64ubnJZaEm7uCq5bQL7UG3AYNrvThgdZdT0tLSeO+99/j111/p3r073bt3Nw3KffPNN/Hx8eHGjRv4+PiYelJeffVVFi1aRGhoKJ9//jkffPBBtXGcOnWKpKQkPDw8sLa2RqvVcuXKFfbv38+KhQtY8NJsPn30AVP7/g+UL91fX1y8B3Dt1KbqGzZj6TkX2JDwOUcubiG83YRy+20cHcnNyWr8wJqAW+NX/Pxqvtp4Y6v1oNv4+HjCw8MpLi7GwcGBNWvWEBgYCMC8efN48skn8fHxwcLCArVazfz584mMjKz0eF27dmXRokWEhISQm5vLJ598woABA4iNjaVTp06V3m/u3Lm89dZble4XoipSj6Vpy0q/RHZGGhGTH6rzMaq6nGI0GvjgwbEAPP3f78uU8X/zzTd58803yx3Pzc2tzEyj6mi1WtLS0hg5suJqrltPx5KWkV5m24mdWxk68+kaP0ZtOHTqzrVtm3HvGlXviyxWt4TA4cOHefzxxykpKWHGjBnlBg9PnjyZ5ORkU2/8nDlzWL9+PVZWVvTp04evv/66wiUNaiqn6DL7kn7ExaYVY7rMRqOxrLCdg5Mr+S00YUlKSsLLy6tJl3uo9V9tly5diImJ4dChQzzzzDM8+uijJCQkAKUJy6FDh1i3bh1Hjx7lww8/5Nlnn2XHjh2VHq9///488sgjhIWFMXDgQH788Uc6d+5cppBTRV599VVycnJMXxcvXqyyvRC3CwgI4OrVq2Rltcx/Tk1d7I7NaKys6dSv/me16LVafn7/HdNtjUXFb153av/+/QwYUHn8o2a/QORDM8psO7Z1AxcT4hskHgCv7tNIP1D1RIjaulXz5pdffuHYsWO899575V5Xzz33HMuWLeP06dOsX7+eEydOmPZt3769TKVbgNGjR5OQkEBcXBw6nY7FixfXKbYDST+y/uQ8YtN3MiRgGpH+D1WarAA4urhT2ALHsBgMBs6fP4+/f+MXEKyNWicsVlZWdOzYkd69ezN37lzCwsL45JNPKCoq4rXXXuOjjz7ivvvuIzQ0lOeff940Ar/GAanV9OnTh3PnzlXZztra2jRb6daXEDXVqVMnrKysiIuLM3coogLW9vYYtCWkxFV/ebi2TuzaTtKxwwB4+Plj0wAVPZOTk3F1da3y06pKpSJsZFS57Vs+eb/e47nFprUfdk7+ZJ3aXn3jGqqu5k16ejp6vZ7Q0FAsLCyYNm0a69evB0Cn0/Gvf/2r3DiikSNHotFoUKlU9OjRo9YVfm85f/0EY7s9z6CAaThYu1fb3tHZDW1BQZ0e62524cIFCgoKyhVLbGruuA6LoiiUlJSg0+nQ6XTluho1Gk2tlitXFIWYmJgm/4sTdzcrKysCAwOJi4tj8ODaj5EQDatDWE+if1xCzpXLNb5PYU42ljY2WFr/b4bh76vHAlw4+r8CamNfqNvU5KoYjUbOnDlT7dg9ABsHB/68YgMAW774Dyf37CQ3O4tLpxNo2zWw3mMDcAsbw8Vdn2LnGYiN252vw1RdzZuK9u/ZsweAjz76iEcffRRHR8cKj63X61myZAlffPFFnWIL8xrAkYtr6ec3sUbtbR2d0OUW8OuaH9FYWKCxssLC0goLS0s0lpZoLEtva6wssbCwvLn/5nYrKzQWllhYWaLWWNxV/1Pi4uJo1apVk5/wUquE5bXXXmPMmDH4+vqSl5fH8uXL2b17N1u2bMHJyYnBgwfzl7/8BVtbW/z8/NizZw+LFy/mo48+Mh1jxowZtG3blrlz5wLw1ltv0b9/fzp16kRubi7z5s0jJiaGzz+XKXiiYYWGhhITE0NaWhq+vr7mDkfcxsHVHVQqUhPi6Dmm+hlCp/btYtNnHwLg6d8RZy9vMs+dJvfqFQZMmU7/SVMwGgx8NnMKuttqPG2f/wUPvv7Per0sFB0dTZ8+fWp9P9+gUE7u2QnA8jdeJnLKdPpOfLBB3vjaDn6OlK3/xG/Ua3dcrr+6mjeV7b906RLbtm1jx44dlc7YmzNnDhEREeWmpFdHqy9mRdy7dPXoSR/fyktq/F6bTl2wdnHi1w2rMOoNGHV6lErWf6qSCtQWFqgsNKg0GlRqNSq1CpVKjUqtRn1zm1qtQX3ztkZjcfO2Bo2m9LtKUVDpDXDr13mrVtmtB1Gpbu5ToVKV7r95638/q8r/bDo7KhVGIKHQQBsrNRs/mlu6T1FK76MoKEYjRqMRxWjAOzCEPhP+N0i8sdXqL/Xy5ctMnz6djIwMnJ2dCQ0NZcuWLaZBZcuXL+fVV1/l4YcfJisrCz8/P9555x2efvp/g8hSU1PL9MJkZ2cze/ZsMjMzcXZ2pkePHuzdu5e+ffvW01MUomLt27fH0dGRuLg4SViaGEd3D9zatuNK0oUatm9l+vly4nkuJ5433dZrtaafbyUrGisrJv3fG6x65+8cXLWcgbdNLb4TN27cQFGUMmsW1VTQ4OEU5uWy9/sFAOxf8T0lBfkMfGRmvSctarWathHPkLbnE9oNu7Nepupq3lS2xEBMTAwJCQl06NABvV7P1atXiYqKMg2M/vzzzzl9+jQbNmyodUzZxZdRKUa6eQ6s1QBj785dee6L78tsU4xGDHo9ep0Wg06HQadDr9Oi1/7utk6H4dZ2vQ69VodeW4LRYDC96RuNBvR6LXqDHoNBh96gR2/QYTAYMBj0pq8Sox7FoEN3/TpOVk5Y2TrcTPyU0mSCm4mgsXSbKSlUQPl9m1s/l264efPWsSBHY4XRyh7rnCzyFOPNZAVAhQKoVSpUajVpiee4fjHVrAkLSjORk5OjAEpOTo65QxF3ka1btyrvvvuuotPpzB2K+J1f165SPnhwrJIcd7xG7bMyLikb5r2vfPDgWNPXF09MUxRFUQwGvRKzbZPywYNjlXmPPaDE/bJNURRF+WXR18rH0ycp2Zcz6iXmbdu2KQaD4Y6OsfW/n5Z5Dgd/XFIvsVUk+8wBJfPoj3d0DJ1Op3Ts2FFJS0tTcnNzlY4dOyrXrl0r06ZXr15KbGysotPplD59+ihxcXFl9iclJSm9evUy3d6wYYPSo0ePO/p/XqTNVZYc/Uel+9evX6907txZ6dixo/LNN9+U2//ss88qrVq1KhOXoijKww8/rISGhirBwcHK008/Xe58//nPf1bc3d3rHPftSooKlP3L/1Mvx6rMokWLlIULF1bbbvkrLylr3vl7g8RQ0/dvWfxQtGhhYWEUFRVx4ULNPsmLxuPoXrvFBV29vBn7whxsbq66a2Vvj5NnG/KuX2PRnOfZMf9zAgePYPbniwgZWtorHPHANKztHdn81Z1VrYXScQBt27a94ynDI2Y9Q+TUR023D65ayldPTCN2+yaMdbk8UQXnzhEoxSXkXjxa52PUZAmBzz77jKlTp9KlSxeioqKqHaP44osvcv36dQYNGkT37t155513qmx/O6PRyJ7EFew4t5hW9t4VtqnJzKZp06aVWXrmli+++ILY2Fji4+PJyspi7dq1pn0JCQlkZmbWONbqWNnYoUJFcUHD1BnLzc0lKSmJ0NDQattmX72Mm1/7BomjpmTxQ9GieXp64unpSWxsbJMuSd0SHdu6EXdfP/xCutfqfsV5ubh4eZOdmY4K+OGvfwJgylvv4dM1qExbazt7BjwwjW1ff0pe1jUc3Wq/AjPA0aNHsbS0NNWkuhNqjYZ+Ex7As0MAq/9VWq+kIC+XHfO/4Lc1K+l+7wS6RQ7Bzsn5jh8LwCviEVK2vouthz+Wtq7V36EC1S0h0L9/f06ePFnp/du3b1+mIvr58+crbVudIxfX0t45gHYdHkClqjh5vH1mE2Ca2TR16lRTmwEDBlRYq+nWjFS9Xk9RUVGZy3Uvv/wyX3zxBVu2bKlz/L/Xe9JTHFr+MYOmv1Jvx7wlPj4eCwuLav9uSwoLyM/Lxd2nXb3HUBvSwyJavNDQUM6cOSMLbjYxHXv24XpaKpdrOI4FQFtUiMbKGm1xEQAZ506jsbDkkXc+Kpes3NK5/wAsbWzZ9d38CgeIVufQoUM4OTnV6FNqbbQP68kTn84naOhIVDd7bUpKitmzeD5LXv4DWelppMTFkJVetym/t/Md8hJp+z6v0/Nvaq4WXMLPvXelyQrUbjXvikyePBlPT08cHBxMidqKFSvo3bs37drV75u6laUN9t5+5GWm1etxobRXsEuXLtWu3XdrTFjrDuWXM2hM0sMiWryQkBC2b99OQkICPXv2NHc44qY+903iyPqfOLZ5HWOe/WO17TPOn2HTZx+hUqmY/Ne3sXV0Qq/V4ujuXuUsIGs7e4Y9+iRbv/qElLhRtA8r+zegGI2cPriX3KtXuJKcSJuOnVFbWFKiKBRZ2+Po5FRlVe474dzai3uefpHuI8bw03v/oCg3G2t7B3JvXGfhH/83mWH0My8RPGREnR9HbW2DV/A0Lh38Cp8Bz9RH6GbT2t6XX1PWVDmVuaLErDYDm1etWoVWq+XRRx9l586dREREMG/evCqLpN6JkqyrWNfyEml1MjMzuXz5MsOGDau2bUrsMWxs7fAwcw+LJCyixXNycqJDhw7ExcVJwtKEqDUauo8ay2/rV9OpbwQde1c8tfV6Wiqbv5rH5XOncfJswyP/+g/uPrWb9dVt4FC2L/iCzAvnyiUs548cYtOn/yt+efbQftOUT2tHJ+578eXaP7la8urYmcf/8yXnj0QTs30zVy6ULay59cuPyUpLpd+kh7CuY2l1W29/rDJbk58ci0P7sPoIu9EdSF5NTtFlLuYmVpmw1GQ17+pYWVkxYcIEfv75Z7y8vDh//rxpQcwbN24QGhpaL4UpL6ecxtrWESvLqntBaisuLg5bW1sCAqrvNUk5eph2QaGmnj5zkUtCQlA6+DY5OZns7GxzhyJu0+e+SfgEhbL2/bdZ9JcXSIo5avp0nJV+ibX/mcuiOc9RlJPNuD+/xqyPv6p1sgKgsbCgdYcAkmKPldvXpnNX2nYNxMXbB5VKjdrCAmfPNnSNHIKNnT2r3/k7B1cuxWis+YDYDRs20KVLFzp16sT8+fPL7X/uuedo3bo1vXv3Nm2zdXAkZOgopv/rP0Trrfn+VKpp3/fRx5j6wh/p5OeLd5s2TJgwoXa/gJta9ZhAdvL+Ot3X3NJzTlOgzSaq27M81a/q6uo1Wc27Inq93jSuxWAwsHHjRrp27UpISAiXL18mOTnZVOW4vqpoK1YWGIoKa1WAtTpGo5H4+HiCg4NrtEZTUV4ezt53XmTwTkkPixBAt27d2LBhA/Hx8QwcONDc4YibrO3suP+VN7lw9DCH1qzgp7lv4OrTDl1xMfnXrmBt78Cwx2YTPGwUllbWd/RYNnb2JMccpTg/v0y5fgdXNx56698A5F69wrnDB0mOj+X0/t0ABPTuR/Sqpeh1OgZNe7SiQ5dxa4bKrl27cHJyomfPnkyaNKlM7ZZp06Yxc+ZMnnrqqXL33759OxoLCyysrHjhu5Vs+++n3F5FZuXxU4wdM6ZOvwOVSsPNih13HW/nruxP/gmDUYdGXXUhwNtnNhmNRl5++WXTzKb58+fj7e3NE088wcaNG7l+/To+Pj58+umnREVFMXXqVPLz81EUhUGDBpWpM9YQvNp0xNizkKMbvqXPuCeqv0MNJCcnk5eXV6NxVzlXMsnNzTb7gFuQhEUIoHRtqq5duxIXF0dkZORdVVa7uVOpVHTs3Y+AXn1JPHaEX9etpm3nrvh37037Hr3uOFG5ZciMJ1gUc5S4nVvoO35yhW2cWrWm19gJ9Bo7gRO7trP1q09o5deBq6kpXL9UswVY72SGyq21d+bNm8fjjz+OlY0t9774Mt1Hj2XFG/+H3mDgRGoabYwltf8F3OWMRiMGxYjBaKg2YYHqZzZV1PMFpZWMq3Pt2rVq29SGd0Ao53avIzvnMi7Onnd8vNjYWNzc3PDx8am2bfzOrVhZWdG5ARYirS1JWIS4KTQ0lKVLl5KZmUmbNm3MHY74HZVKRUCvvgT0apgq2K5epTU79i1dhNFgoPvosdjYV7wwoqIo7F32HQAde/fn0OrlREyeWmHb37uTGSqVrb2jLylNUE5nXsXP3YWM40dqdLyKWLu2pfDSOezaNsxA4vqm05ewJ3EpuSVZDPOfipVF/Y71aApuXLuESq+vl2RFq9Vy6tQpIiIiqv1gZjQYiN++ha4Rg7GsZiZRY5CERYibAgICsLOz4/jx45KwtEAqlYqQ4aPJz8ri4KqlRP+0nG6RQxk07dFyNU+yM9MpysmmS/hADHodAKcO7CVo8PBqH6euM1SqWnsn8/xZAGIvZtDd15vcnBvkXb9WZfE9XXExFxPi0RYV3h4IRoMjF0/+SM9pf602JnOLT9/B+etxjOg0A0eb+p1F01RkXjzN6e2rGPB4/dRhOXXqFFqttkaXg5JijlJYkEfo6LH18th3ShIWIW7SaDT07t2b6OhoBg8ejL29vblDEo1IpVYzavYLABRk3yBuxxaOrP+JpONHmPb2Bzi3/t+nW0eP1jh7eXMmeh8devSm//1TObx2JSWFhdXO0qnrDJWq1t7pPvpedi/9jrOXr3F/r9Iqsl8/+xgz3v+MVu3am45RlJ9H4tHDnN23i5SEExgM+kofL2BEJs6tm+7qveevHuFKwSUmhvzJ3KE0mDPRm8jPy6L3hCextLrzHg6j0ciBAwcICAio0XpX8Vs24OHlbfb6K7fILCEhbnPrjePXX381cyTCnOxdXAmfPJXHP/oSlUrNzx+WLQ1vYWnJjPc+AeCXRV8TNHg4Rr2e1XPfqPbYdZ2hMnbsWDIyMkhOTmb//v2EhISYxlzYODjQ8aGZDBk6lH5R/xuXsePLT7icdIHjWzew4rU/8cWsqWz54j8kxsfg1bEzj334JS98t7L0a9GPvLDoR578fCEAaacqr0xrbr+lric99xzDO1U/yPlupNWVsHfhO+gMOnqNegQHtzu/FARw7tw5rly5wqBBg6ptqygKGefPEhA+sMmM6ZOERYjb2Nvb06tXLw4fPiyVbwWO7h506NGLksLCcvusbGzpGjkEbWEBRoMez4DOZJw9hU5b9YDXmqy988QTTxAeHk5cXBw+Pj6sWbOm2lhXrlrF9MceZ/isZxjy6GwA0hPP8cMrL7Jr0dekXThbpv2lMwms+MernDm4DysbW6xs7bCytcPJoxWu3q1JPXm8pr+mRnU5L5Hs4msMCphm7lAaTOwvK+g5/kmCI8fX2zEVRWHv3r20a9cOPz+/atufPrCHwsJ8fLpVXCHaHCRhEeJ3IiIi0Gq1ZdY2ES1Xwp6d5F3JZM8PC8nPul5m35DpswD4+YN/0nf8ZFQqNav/9fdqFykcN24cZ8+e5fz588yeXZpcbNq0ifTjh/lwyr2M7eRL/OFDaLVa0tLSmDixbBG036+9A6Wl4adMmYJKpaJX1DhG3ry8BWBxcyZVm87dGDfnr/zhu1Xc98dXKMrJJuHAnnLxdQkfypmD+8o936bgaNpWhnWc0WDH1xsNfHN2Fx+f3MhHJzfy9vElZBblNdjjASg6HYbsbAovpnDtxDG0Kck4uLWu18dITk7m0qVLNSrbELt9M1u/+A9d+kbgF9qjXuO4EzKGRYjfcXJyonv37kRHR9OvXz8sLaufIimarwFTpnNi9w5itm3k2Kaf6TJgCL3Hjqd1e3/sXVwZ9fSLbPvqE6ysrek2aCgJe3byn2nj+fOKDbV+LPXNrvfEY0dIPFY606cuxwEIHT4a1zbepJ85RXFBAa382tMlfCAaCwuuJCey5ctPcGzVmqgKlj3ofe8kjm1eS/TqJYx88g91evyGo5RWGq6jn1KOklF4nbjrp3goYDSZRTeY6h9u2v/Z6a1EegbR2720F0Jn0LM06SDphTfwtXNltHcgJbpCivOyKcnNpjjvBrr8XCgsRqU3QC1D23c4jk+/+wkjMH36RKY+8iCBD8427T98+DCPP/44JSUlzJgxg7//vXRBzOLiYp5++mmio6NRq9V88803REZGEhsby1NPPUVxcTH29vZ8//33+Pv7s3fvXry8vOjYsWOV8VxJTmTH/M8JHjSc4bOfbzKXg0ASFiEqFBkZyfHjxzl+/Dh9+zbMNFpxd+g7fjJ9x0+mpLCAmG2b+G3jz5zauxM3Hz963nMvocNHs+2rTzi0dhUP/u2fnDm4F4NOV6fH8u/Zl13ffVNvsfsGhuAbGFJu++YvP0ZXXMRTXy7C2q784HJrO3v6jn+Agz8uoe/4KWUGHJubv1sYZ69E09Wz9nVBdEYDZ3Mu8kroBG6URHD4ejJWGku+PbeHmZ0Gk6crplBfYkpWACw1FjzasXTMR0pBNge++ozgoK5YOzjj5OSKjVdbbB1dsXBwQGVlVat49Ho9018O5ODho6YignNefgdXl/8NiH3uuedYtmwZgYGBhIeHM2nSJIKDg/nnP/9J586dWbRoETqdjoKCAgD+9re/8dZbbzF69Gi+/PJL3nvvPV5//XWSkpJ44IEHqk1AflvzIw5Ozox46gU0NaiC25iaVjRCNBFubm4EBwdz4MABevXqhUajMXdIwsys7ezpN+EBet87kaSYoxzfuoEd8z/nwI9LAHBp7YmiKKg0FthVkARUJOVELMe2rEdfUoKltQ0XjpQtStaxb0S9Pw+DXse15ERsHJ0qTFZu6XnPeI5u/IkDP35H1PMNv15STbVyaM/pK7VbPiAuK5mVqfG0trbiyS6li0S6Wjsw2jsYgKWJB/n45EZATYhr5eM7/OxduBgcQdve3bGtwSyb6lRXRDA9PR29Xm+agjxt2jTWr19PcHAwP/zwA6dPnwbA0tISFxcXoHSKfF5e6SWsvLw82rRpw759+3B3dzetdVSZvKxrnD58kEEPP97kkhWQhEWISkVGRvLll18SHx9P9+7dzR2OaCI0FhZ07N2Pjr37kRRzlI2ffghA73snorGwwKlVa7IuprBwznOEDBlB73srXoSvKC+XVW//FZVajfK7dWJUKjWKYqRD9/pfjPPWAnY977mvynaWNjb0mziNPYvn02/iVNzb1n6NpoaQr8vD3tKx0v2KorA08SA3tAWobl6fKdIX83b3yp/vNP+aJ4Z9hg/m6JbtRIy9p+ZBV6K6IoIV7d+zZw/Z2dlYWFgwZ84cDh48SFhYGPPmzcPR0ZF///vfjB49mpdeegkHBwc2bNjADz/8wPjx41FXs3jhsQ0/Y2lhQciw6metmYMMuhWiEp6ennTp0oV9+/bV68Jjovno0L0Xsz+bz9P//R4P39JP5vc89QcCh4wg62IKe75fQHLMUVP7wtwcigvy0Wu1HNu0FqBMsqK2sKB1QGee+3Y5f16xgdDhd/6m+HtqtQa/sF4kHNxbYRG724WNjMLOxZH9KxbWexx15WnvQ3peSoX7Dlw5zzuxqwlx9+f5bqN4rttInus2kjkhVSdntWGlVlOcnVPt764mqisiWNl+nU7HhQsXGDNmDMeOHaNNmza8++67AHzxxRd8+eWXpKWl8cILL/D000/j7OxcbaG43GtXOb51A93vua/OK343NElYhKjCwIEDuX79OqdOnTJ3KKKJsrK1w97F1XS7TacujHnmJZ787FsA1n/8LtfTUtm/4ge+fPJhPp/5EJ9Mn8Shn1Zgbe+A3W3jFVq1DyB80hSy0i+SeeFctVOk6yps5D1kX7pIdmZ6le0sLC2JeGAG5389zJXkxAaJpbaK9flYasoPhF+feoyskjz+GnY/oS4NV6lapVIROGoEv27efsfHqqiI4O1Vtivb7+HhgZOTE2PHllagnThxIjExMQAsW7aMqKgoAEaOHElsbCwRERHVXtbe//0CrKys6Ttxyh0/r4Yil4SEqIKPjw8dOnRg3759BAYGNqkR86Jps3d1xS+sF6knYlj052cB6NCzD0GDhpOdmY6btw/eXbpRmJtDdmY6xfn5/LbxZ9a+/7bpGGqNBq/O3ejcJ5z2YT1x96n+skxJYSHrP3mPouwbRD40gw49epdr0z6kBxora07u/YXIKdMrOMr/BA0ewa8/L2Pf8gXc/8o7VbZtaCczfuHstRjGB71UZrvWYOBEdiqvhk5olDi8WrmT4eLMgY1b6T18MNZ1XGfn9iKCTk5ObNq0yTQLCMDb2xuNRkNcXByBgYEsW7aMBQsWoFKpGDVqFNHR0YSHh7N7927T+BR3d3cOHTpE//79+eabb/D09KRnz6ovLV5Pu8ipQ/sZPuvZJtu7AqBS6qNfqwnIzc3F2dmZnJwcnJyczB2OaEYSExNZvHgxDz/8MJ063R0LwommQ6/VknoytvRSTGiPKpNeRVHIuZyJtrgIo17PpTMJnDkczeXzZzDqSxdkHPTI41WuUK0tKuTTxx4ss+2Rdz/B83fl1Xd++xVxO7cw8f/eoH01tTZO7dvFps8+ZOrbH+DduWsNnnX9upBzkd3px0jOSaaNc2C5/QoKD7TvR2tb5wru3XCysm5w/sgx+o6ufg2pyqxbt445c+ZgNBp5+eWXmT17NlFRUcyfPx9vb28OHTrErFmzKC4uZvr06bz55ptA6f+l6dOnk5eXh5+fH9999x1ubm7s2bOHl156Cb1eT15eHu+++y4PPfRQlTFs/exDEo/9xuyvF6OxaPwyDjV9/5aERYhqKIrC/Pnz0Wg0zJw509zhiBZIr9VyfOsG9i9fjIObB/e9+DJeHTtX2v7c4YOs+/BfZbYNnj6LXlHjTYNu9VotP733DzLOneKht/5dLqG5ndFoYNGfZ2Pv4sKUNz6snydVA1qDjpVJ++lg705Em+oX6zOHQ3sPYG1pQY/w6teDakxbt27l2LFj/PGPf8Smih6gguwbfP3MY0Q8+DD9Jj5YabuGVNP3bxnDIkQ1VCoVAwcOJDU1tdwquUI0BgsrK/rcN4kZ732KpY0NS/82hyPrf6q0fae+ETy/cEWZbXu+X0D62dNljjnhL3/F2bMNq+e+SVFebqXHU6s1DJw6i7SEM6SeiLvzJ1RDWy4eYVy7Pk02WQHoP2gAuuwcrl6/Ye5QTAoLC/ntt9/o169flckKwPFN61Cr1YSNjGqk6OpOEhYhaqBz5860bt2aXbt21cvsACHqwt3Hl+lzPyZsdBR7f/iWy4nnK21rbWfP+Dl/K7OtdQf/MretbGy5/5U30RUXsWPBl+WmV9+uY59wWrX3Ze/SrxvlNWAwGig26HG0cmjwx7pTvUePIGbTFvLz8s0dCgAHDhwAqHYVcKPBQPz2TQQNGoaNQ9P/PUvCIkQNqNVqRo4cSXJyMidPNt1VbEXzp7GwIOKBhwE48OMPLH7lRT6cci8fTR3Hwj89g66kdNFObVEhG+b923S/toEhWFqX/7Tt6O7ByCef42z0frb8d16lSYtKpWLQ1Ce4fCG5zFTthrIt7TdGtg1r8MepD2q1Gs++fchpAj2wV69eJTo6mgEDBmBvX3UBw6SY3ygsLCBkVNPvXQFJWISosU6dOtG1a1e2bt1KSUnDTDcVoiZsHRzp0LMv2VcuczXpAlBazyXr0kXmzZhMSWEhiqJg0GpN9wkeXPnA0MCBQxn9zIsk7N7Jpi/+g9FY8eKNfmE98erkz95l8xu0lyUh6wIFei2uNo07iLau8nLzSNn5C95du5g1DkVR2LRpE87OzgwYUP3SBXFbNuDh6V3l+KWmRBIWIWrhnnvuoaioiN27d5s7FNHChY24hxuXLjJs5tPl9n32+IN89njZehqH166q8njBQ0Yw5vk/cXr/bo5vqXjBxVu9LNdS0jj/26G6B1+BfG0Ba5MPsCpxHzqjgcn+pasKb9iwgS5dutCpUyfmz59f7n63ytt37NiRf/zjH6btjzzyCGFhYYSEhPDMM8+Yij/OmTOHLl26EBISwsyZM9Hr9XcU9/EDh4jZs4+o2TNRmbmc/YkTJ0hKSiIqKqraRVsLsm+QdCKWkDH1V1SvoUnCIkQtuLi4MHjwYA4dOsTly5fNHY5owfx79MYnKJRfvv0KjZUVKlSMb/c8blZli6bN/nIRANb2pWMUSgoLWfvhO6x8+69oiwrLtA0cOJTO4QM58OMStMVFFT6ub1Aobbt1Yd+yBVWOeamNAl0hK5IOMtqnN5P9BxLmUToDSq/X86c//YlffvmFY8eO8d5775GVlVXmvrcWBzx9+jTr16/nxIkTQGnF19jYWOLj48nKymLt2tLKwqNHjyYhIYG4uDh0Oh2LFy+uc9xFN26Qn5fPwPuizL72TnFxMVu3bqVr1641Kr+QsG8XKlR0Gzik4YOrJ5KwCFFL4eHhuLm5sXHjRhmAK8xGpVYzeNpjoFLhFdCZgK59sdHYM7LtDFysWpvaaTQWeHQIoKSwgOzMDE7u2cH5w9GknojlxK6y1Vq1xUVcTU7EqNdRnF/5ANKBU5/gxqVMzhyq3SKElVmfepjpHYdgY1G2vsztiwM6OjqaFge85fbFAS0sLEyLAwKm6bF6vZ6ioiJT/ZuRI0ei0WhQqVT06NGjTCXZ2rJxcaE4M5OkC0l1PkZ92b17NyUlJdxzT/XLOSiKQvyWDXTs0Rtbh8rXZWpqJGERopYsLCwYO3YsqampxMU13hRPIX7Pq2Nnnvn6B6a8MZfxb72Off/S3pXRbR9npN+jdOwzADtnF8InPMCNSxdZ8OKT7Fr0ten+oSPGlDnexnkfkHf9KpP/+jZOHq0qfdy2XbrRLjSY/Su+xWioeLxLTcVfv0CAgwdWFZTbr8vigLfvnzx5Mp6enjg4ODBu3Lgyx9br9SxZsoRRo0bVOXaVSsXIxx7hRkYmMfujq79DA7l8+TK//vorgwYNMq3aXJWMc6e5ce0KIaPHNnxw9UgSFiHqwN/fn6CgILZt20ZRUcVd50I0BjsnZ1PvgeuEjjiOKC3f76b2YnjkowAE9O6HvZtHuftaWFmZfi4uyCfx6GHCRkbh0y242scdNPUJcjKvcWr/7jrHbjQaibuRSh/Pih+vrosD3rJq1SoyMjJQFIWdO3eWaTdnzhwiIiKqnfpbEz0jw7l89hxJ5xt/vSVFUdi4cSPu7u6Eh4fX6D4xm9bh6OyCX0j3hg2unknCIkQdjR49Gp1Ox65du8wdihAmTsP8ULe+OZ7i5n94jYUl4/74f+XaFuXnmX62sXcgaOgI4nZswaDXVfs4nv4d8e/VkwM/LsJQx4Gr2y8dYaR3SKX767o44O2srKyYMGECP//8s2nb559/zunTp/nPf/5Tp7gr0nfCfZz8v1fr7Xg1FRsbS2pqKlFRUVjUYBxNcX4+Zw9HE3bPfaaqx3eLuytaIZoQJycnhgwZwpEjR0hPr3rVWyEai0qtwuvZPnj9pTcOfb1N2707d+OPS9fyp+XreeD10rL9S/82h9yrV0xtQoaMRFdcRNaltBo9VuRDM8m7fqPcWJiaMBqN3NAW0dqufM/PLbcvDpiXl8emTZsYPXr0/57TbYsD6vV6li1bxn333Yderyc5ORkAg8HAxo0b6dq1dA2kjRs3smDBAn788ccavcFXR1EUigoKcHF1QeXvX/0d6lFRURHbt28nODgY/xo+duz2jRgVI8HD6n4pzFwkYRHiDvTr149WrVqxceNG07RJIcxNbWOBhbtt+e03B5u2Cw7lgdffQVdSzJLX56ArLi02d6vaqfbm7eq0ateeTn37Eb36+1qPZfnl0lGGtKn60pOFhQUffvghQ4cOpUePHvzlL3/B3d2dqKgo04eEzz77jKlTp9KlSxeioqIICQnBYDAwdepUQkJCCAsLw8HBgaefLp3+/eKLL3L9+nUGDRpE9+7deeedO1uBetfKNRw78Cv7N2yh0yMP39GxauuXX35Bp9PVeBxOYW4Oh39aQdiwUdi7uDZwdPXPvPOwhLjLaTQaxo4dy8KFCzl+/Di9evUyd0hC1Ei74DAeeuNdvv3jUxzfuoG+4ydjc3PGSO61K7Tt0q1Gx+nQox/nfv0Vg16HWqOp0X2MRiNXtQV4VdG7csu4cePKDZjdtGmT6ef+/fuXqz5tbW1NdHTFg2DPn698OYPqJF1IJC0+Ab1OT9fwvmh1OjQaDQNGDavzMesqPT2d3377jVGjRtV4wd89i74GVIRPmd6wwTUQSViEuEN+fn6EhYWxY8cOunXrhp2dnblDEqJGXLzaEDRkBNE/LcfBzR1Xr9JLSLsWfU23AYNrdIzszAysne05V5BBca6eYoOeEqOeYqMBvdEAigGDYkCFkVtDZK8UFzKmbeVjV5qqrKxsOnUPwdXLk3MnT6PRqBk4sfELrxmNRjZu3EirVq3o27dvje5z4ehhEg7sYdRTf8DO6e6oIPx7krAIUQ9GjhzJ6dOn2bFjR7lPg0I0ZYOmPca1tIts/uxD07ai3ByKC/Kxsa9+QbyMS2moW3mhxoiblRU2ajusLSyx0VhiqbZEpbJErbZApapZ70tTcvnqNc6t3UDIg/cTs20nlg4OePXpCUBwr+5mi+v48eNcunSJxx9/HE0NerWK8nLZ9vlHtA8MIXjoyEaIsGFIwiJEPXBwcGD48OFs2rSJsLAw/Pz8zB2SEDVi6+jEtH/8m6K8XPKuXeWH1/4IikLetas1SliupiTTpWdvurrdHevR1MaZA9F0uS+K4zt3EzZkIK4e7uYOiby8PHbs2FGr/zM7/vspBr2e0S/MKTPt+25Tq4Tlyy+/5MsvvzSNvg4KCuLvf/87Y8aUFh/Kz8/nlVde4eeff+b69eu0b9+eP/zhDzzzzDNVHnf16tW8/vrrXLhwgYCAAN555x0mTpxYt2ckhJn07t2bEydOsGrVKp5++ulqV0oVoqlQqVTYOTlj5+TMQ2++x9avP8XFq02199MVF1N8NROv9o07O6axOLm5UVBQyBAzXPb5PUVRKCksYMWypahQ6OjhSuz2TZQUFmLU6zEYDBgNeowGAwatFoNOh1GnI/f6VVIT4hnz/J9xcDN/wnUnVEotaouvX78ejUZDx44dAfjuu+94//33OX78OEFBQTz55JPs2rWL+fPn0759e7Zt28azzz7L6tWrGT9+fIXHjI6OZuDAgbz99ttMnDiRNWvW8Pe//539+/fXqqBPbm4uzs7O5OTk1HgAkhD1LScnh//+97+0bduWqVOnor7L6hwIURvpZ0+z7PU5PDL3Yzz9O5o7nAbx69Yd5GXn0DawK91Cgur12IrRSHFBPoU52RTmZFNw83thdjb5V69QcP0ahdlZFBUUUFRUQIFLa7QebbBNPYNFYT4qlQorK2vUajVqjab0u/rmd40FGgsL1BoNvqE9GDhjVpPtXanp+3etEpaKuLm58f777zNr1iyCg4OZMmUKr7/+uml/r169iIqK4u23367w/lOmTCE3N5fNmzebtt1zzz24urqybNmyGschCYtoKs6dO8eSJUsYMWIEkZGR5g5HiAYTu30zO779ihe/W1mmam5ztHvVz3Tt1wcv37ZVtjMaDBTm5piSkFuJSMH1axRcvUJB1nUKc3MoKiqkuKiwXDkEjUaDrZ09do5O2Lm4Yu/RCns3dwpRE33mAj2DAono3w87ZxdsHRzvuuJvFanp+3edx7AYDAZWrlxJQUGBqRxwZGQk69atY+bMmXh7e7N7927Onj3LJ598UulxoqOj+eMf/1hm2+jRo/n444+rfPySkhJKSkpMt3Nzc+v6VISoV506dSIyMpKdO3fSrl072rVrZ+6QhGgQ6UkXsPXybvbJCkDXiP4cXPkjYZH9SxORG1nkX8mk4No1CrJvUFRYQFFhAcUVrHJtaWmFnb0Dds4u2Lm54+rfEXsXF+ycXbFzdsbO2QV7ZxfsnF2wsrUr1xOSl5fHV199hb+/P/feP7nF9tzWOmGJj48nPDyc4uJiHBwcWLNmDYGBgQDMmzePJ598Eh8fHywsLFCr1cyfP7/KT5mZmZl4enqW2ebp6UlmZmaVccydO5e33nqrtuEL0SiGDh1KSkqKaTyLTHUWzVFaciJtmun4ldtpiwrZ8+3npMUfJ+ngNgBsbGyxdXTC3sUVRx9fPF1dsXd2xdbJ+WYy4lJ629kZSyvrah6hckajkdWrV6NSqZg0aVKLTVagDglLly5diImJITs7m9WrV/Poo4+yZ88eAgMDmTdvHocOHWLdunX4+fmxd+9enn32Wdq0acOIESMqPebvs0lFUaq91vbqq6/ypz/9yXQ7NzcXX1/f2j4dIRqERqNh8uTJfPXVV6xZs0bGs4hmRzEayU9LoXt4877sadDr+OqZx9AVFdI5fCBDZzyBrZMzmnoo618Te/bsISUlhRkzZuDgUP2sreas1r9xKysr06Db3r17c+TIET755BM+/vhjXnvtNdasWcPYsaVLVoeGhhITE8MHH3xQacLi5eVVrjflypUr5Xpdfs/a2hpr67pnrUI0NGdnZyZNmsSSJUuIjo5mwIAB5g5JiHqTfTkDY0kJrfw6mDuUBnV8ywZ0RYUABA8Z0agzbRITE9mzZw9Dhw6lQ4fm/XuuiTtOERVFoaSkBJ1Oh06nK/cpUqPRVLnGSnh4ONu3by8zjmXbtm1ERETcaWhCmF2nTp0YMGAAO3bswNfXV8aziGbjakoSAK3vIGHRaUvIPH8WvVaLSqVCpVKjUqtQqzU4uHvg6O7RKD0Zep2OqymJ5FzORFtchIdve1q174CllTW/7d5JQPhAJrxUfrXrhpSXl8fq1avx9/dn4MCBjfrYTVWt/hJee+01xowZg6+vL3l5eSxfvpzdu3ezZcsWnJycGDx4MH/5y1+wtbXFz8+PPXv2sHjxYj766CPTMWbMmEHbtm2ZO3cuULoQ1aBBg3jvvfcYP348a9euZceOHezfv79+n6kQZjJs2DBSU1NlPItoVq4kJ2Fxc6Bobeh1OpJjjhJ3YA8pxw5jvG3yRDkqFZau7jh4tMbD0xP/4DCCh1Q+vKCmivPzST97iounT3Ih4STZSedR9DrTY6IooNbgGRhCwcVkHIMadxkBo9HITz/9JONWfqdWCcvly5eZPn06GRkZODs7ExoaypYtWxg5srTU7/Lly3n11Vd5+OGHycrKws/Pj3feece0SiZAampqmV9+REQEy5cv529/+xuvv/46AQEBrFixolY1WIRoymQ8i2iOUhLP49aufY3aGvQ6UuJjiN+/l8TfDmEsLsK+bTv6j3+Ajn36Y2PvgKIooCgoihG9Tkfe9WvkXr1MzuXLXLmcQUpcDMnxsXeUsBiNBg79vIrolUvBaMDCyYU2XboRNu0xvLt0xc3bB42lFdcvpnDpzCliD+3HxtMb38CqV5Wub3v37iU5OVnGrfzOHddhaSqkDoto6s6ePcvSpUsZOXKkjGcRd715T82g++DhDJr2aIX7FUUh49xpYnbt4Oyh/RgKC7D18iZ0wGC6RQzE3ad2l0cX/v1l7BwcmfLy69U3rkB+1nXWfPoBV06doOd9k+gxYgzOrT2bXDG1xMREFi9ezJAhQxgyZIi5w2kUDV6HRQhRO507d2bAgAGm+iwyq03crQpzc9BlZ+HZofyU5txrV0nY+wvHdm2n6EomVq7u9BgZReCAQXi0a1+nBMGg13Mj8TwhUx6ptI1iNP6vPL2+9LvBoMeo13MlOZFN//0UtcaCB/72T9oFh9U6hsZwa9xKhw4dGDRokLnDaXIkYRGiEd0az7Jy5Upmz54t3b3irnQl6QIArW/WYNGVFHPucDRHf9nOlVPxqC2t6NA3nB6zn6ddUOgdV2O9mpyIotMSd2APsb9GU1KYj76gAENxEYpBj2IwQBWTOwC8w3ox/vk/YefkfEexNBSDwWAat3L//ffLZeMKSMIiRCO6NZ7lm2++YcmSJTz22GMyPV/cda4kJ6K2tiH/Rhb716zk/K8HMBYX4d4lkNFP/YHO/QdgZVt/g8ttnZxpHRSGysICe3sHHBz8sXNwwMrWDo2lZZl1c9QWt362QHPztpWtLV4BnZvc5Z9bjEYja9euJSUlhenTp8sHmUrIGBYhzCAzM5OFCxfi7e3Nww8/jEUjFaESoj4s/9cbXIo9CoC1R2t6DBlB0KBhuHh6mTmyu4+iKGzdupVDhw4xefJkgoMbd4BvUyBjWIRowry8vJg6dSrff/89P/30E5Mnt9z1QcTdJ6BXX2wcHOg9YgxtuwY2iwX4zGX//v0cOnSIqKioFpms1Ib0sAhhRqdPn2bFihX07NmTe++9t8l2WQsh6t/Ro0dZv349gwcPZujQoeYOx2xq+v4tabEQZtS1a1fGjRvH0aNH2bVrl7nDEUI0klOnTrFhwwb69OnTYqYv3ym5JCSEmfXo0YOCggJ27NiBvb29FE0UoplLSkpi1apVBAYGMmbMGOlZrSFJWIRoAgYMGEBBQQGbN2/Gzs6OkJDGLQUuhGgcGRkZLFu2DD8/PyZOnChj12pBEhYhmgCVSsXIkSMpLCxkzZo12NramlZFF0I0D9evX+eHH37Aw8ODKVOmyOzAWpLUTogmQq1WM27cONN6WmlpaeYOSQhRT/Ly8vj++++xsbHh4YcflvpLdSAJixBNiEaj4YEHHsDLy4slS5Zw9epVc4ckhLhDRUVF/PDDDxgMBqZPn469vb25Q7orScIiRBNjZWXFtGnTcHR05PvvvycnJ8fcIQkh6kin07Fs2TJycnKYPn06Li4u5g7priUJixBNkK2tLY888ggqlYrvv/+egoICc4ckhKglg8HAqlWrSE9P5+GHH6Z169bmDumuJgmLEE2Uk5MT06dPp6ioiG+//ZYbN26YOyQhRA2VlJSwdOlSzp07x4MPPiirs9cDSViEaMI8PDyYNWsWRqORBQsWkJGRYe6QhBDVyM/P57vvvuPixYs88sgjdO7c2dwhNQuSsAjRxLm5uTFr1iwcHR1ZuHAhiYmJ5g5JCFGJrKwsFixYQG5uLo8//jj+/v7mDqnZkIRFiLuAg4MDjz32GL6+vvzwww+cOHHC3CEJIX4nPT2dBQsWoFKpmDVrFm3atDF3SM2KJCxC3CWsra2ZOnUqwcHBrFq1iujoaHOHJIS46fz58yxatAgXFxdmzZqFq6uruUNqdqTMnhB3EQsLCyZMmICjoyNbt24lLy+PESNGSHlvIcwoNjaWtWvXEhAQwAMPPICVlZW5Q2qWJGER4i6jVqsZOXIkjo6ObNmyhfz8fMaNGydlvoVoZIqicPDgQbZv30737t2577770Gg05g6r2ZL/cELcpfr374+DgwNr1qyhoKCABx98UMp9C9FIjEYj27Zt49ChQwwcOJBhw4bJqssNTPqRhbiLBQcH8/DDD3Px4kUWLVpEfn6+uUMSotnT6/X89NNPHDp0iKioKIYPHy7JSiOQhEWIu5y/vz+PP/44eXl5LFiwgKysLHOHJESzVVxczJIlSzh16hQPPvggffv2NXdILYYkLEI0A23atGHWrFmo1WoWLFjApUuXzB2SEM1Obm4uixYtIj09nenTpxMYGGjukFoUSViEaCZcXV2ZOXMmrq6ufPvtt/z6668oimLusIRoFs6dO8dXX31FQUEBM2fOpH379uYOqcWRQbdCNCP29vY89thjbN++nc2bN5OYmMj48eOxs7Mzd2hC3JUMBgO//PILBw4coGPHjkyYMAEHBwdzh9UiqZRm8hEsNzcXZ2dncnJycHJyMnc4Qpjd6dOnWbt2LZaWltx///34+fmZOyQh7io3btxg1apVZGRkMHz4cMLDw6XmUQOo6fu3JCxCNGM5OTmsXr2aixcvMmTIEAYOHCj/cIWogZMnT7Ju3TpsbW2ZPHkyPj4+5g6p2arp+7dcEhKiGXN2dubRRx9l79697Nq1i6SkJCZNmiRJvRCV0Gq1bN26laNHjxIUFMR9992HjY2NucMSSA+LEC1GUlISP/30EwaDgYkTJ9KpUydzhyREk3LlyhVWrlzJjRs3GDNmDD179pT6Ko1ALgkJIcopKCjg559/5ty5c4SHhzN8+HAp6S9aPEVROHbsGJs3b8bV1ZUHHniA1q1bmzusFkMuCQkhyrG3t2fatGkcOnSI7du3k5KSwuTJk3FzczN3aEKYRXFxMevXr+fkyZP06tWLe+65B0tLS3OHJSogPSxCtFCXLl1i1apVFBQUMG7cOIKDg80dkhCNKi0tjVWrVlFUVMS4ceMICgoyd0gtklwSEkJUq7i4mI0bNxIfH0+PHj0YNWoUtra25g5LiAZlMBg4ePAgu3btok2bNkyePBlXV1dzh9ViySUhIUS1bGxsmDRpEv7+/mzevJkzZ84wYsQIunfvLtOfRbOUmJjIpk2buH79OhEREQwbNgyNRmPusEQNSA+LEAIofQ3t2LGDuLg42rZtS1RUFG3btjV3WELUi+zsbLZt20ZCQgLt2rUjKioKLy8vc4clkEtC5g5HiLtWSkoKmzZt4vLly/Ts2ZPhw4djb29v7rCEqBOdTkd0dDT79u3D2tqaUaNGERISItOVm5Cavn/Xqs/3yy+/JDQ0FCcnJ5ycnAgPD2fz5s2m/SqVqsKv999/v9JjLlq0qML7FBcX1yY0IUQ98fPzY/bs2URFRZGQkMCnn37K4cOHMRqN5g5NiFo5e/YsX3zxBbt376ZPnz688MILhIaGSrJyl6rVGBYfHx/effddOnbsCMB3333H+PHjOX78OEFBQWRkZJRpv3nzZmbNmsX9999f5XGdnJw4c+ZMmW1SWVAI89FoNPTt25egoCB27tzJpk2bOHbsGFFRUbRr187c4QlRpaysLLZs2cLZs2fx9/dn2rRptGrVytxhiTt0x5eE3NzceP/995k1a1a5fRMmTCAvL4+dO3dWev9Fixbx0ksvkZ2dfSdhyCUhIRpQWloamzZtIj09ndDQUEaOHImjo6O5wxKiDK1Wy/79+zlw4AD29vbcc889dOvWTXpUmrgGnyVkMBhYuXIlBQUFhIeHl9t/+fJlNm7cyHfffVftsfLz8/Hz88NgMNC9e3fefvttevToUeV9SkpKKCkpMd3Ozc2t/ZMQQtSIj48PTzzxBDExMezYsYPTp08zZMgQ+vXrJzMshNkpisKpU6fYunUr+fn5DBgwgMjISKysrMwdmqhHtU5Y4uPjCQ8Pp7i4GAcHB9asWUNgYGC5dt999x2Ojo5MmjSpyuN17dqVRYsWERISQm5uLp988gkDBgwgNja2yrVO5s6dy1tvvVXb8IUQdaRWq+nZsyfdunVj165dbN++3XSZyN/f39zhiRbq6tWrbN68mcTERDp37syMGTNwd3c3d1iiAdT6kpBWqyU1NZXs7GxWr17N/Pnz2bNnT7mkpWvXrowcOZJPP/20VgEZjUZ69uzJoEGDmDdvXqXtKuph8fX1lUtCQjSSzMxMNm3aRGpqKv7+/kRGRtKhQwfpfheN4vLlyxw4cID4+HhcXFwYM2YMnTt3NndYog4abVrziBEjCAgI4L///a9p2759+xg0aBAxMTGEhYXV+phPPvkkaWlpZWYgVUfGsAjR+BRFISEhgX379pGZmYm3tzeRkZF07dpVCs+JBpGamsr+/fs5e/YsTk5ORERE0KtXL1n/5y7WaJVuFUUp09MBsGDBAnr16lWnZEVRFGJiYggJCbnT0IQQDUylUhEUFERgYCCJiYns37+fH3/8EXd3dwYMGEBoaKisBi3umKIonDt3jv3795OamkqrVq2YMGECISEhMoaqBanVf5LXXnuNMWPG4OvrS15eHsuXL2f37t1s2bLF1CY3N5eVK1fy4YcfVniMGTNm0LZtW+bOnQvAW2+9Rf/+/enUqRO5ubnMmzePmJgYPv/88zt4WkKIxqRSqQgICCAgIIC0tDQOHDjAunXr2LVrF+Hh4fTq1Qtra2tzhynuMgaDgZMnT7J//36uXLmCj48PDz30EJ07d5YevBaoVgnL5cuXmT59OhkZGTg7OxMaGsqWLVsYOXKkqc3y5ctRFIWpU6dWeIzU1NQyf2jZ2dnMnj2bzMxMnJ2d6dGjB3v37qVv3751fEpCCHPy8fFhypQpXL16lYMHD7Jjxw727t1Lnz596NevHw4ODuYOUTRxWq2WmJgYDh48SHZ2Nh07diQqKgo/Pz8ZI9WCSWl+IUSDysnJ4dChQ/z2228oikKPHj2IiIiQ1XFFOUVFRRw5coRDhw5RVFREUFAQkZGRsuZPMydrCQkhmpTCwkLTm1FxcTHBwcFERkbi6elp7tCEmeXm5pqSWoPBYEpq3dzczB2aaASSsAghmiStVsvx48c5ePAgOTk5+Pr6EhISQlBQkCyy2IJotVrOnj1LfHw8586dw9LSkj59+tC/f3+5bNjCSMIihGjSDAYDCQkJxMbGcuHCBdPA3dDQULp06SJVSpshg8FAUlIS8fHxnDp1Cq1WS9u2bQkJCaF79+6yhlwLJQmLEOKuUVBQwMmTJ4mLiyMtLQ1LS0u6du1KaGgo/v7+MnX1LqYoCpcuXSI+Pp4TJ05QUFCAu7s7ISEhhISESFVaIQmLEOLulJWVxYkTJ4iLi+PatWvY2dkRHBxMSEgIPj4+MkvkLnHt2jXi4+OJj48nKysLBwcHgoODCQ0NpU2bNnIehYkkLEKIu5qiKGRmZhIXF8eJEyfIy8vD1dXV9Mm8VatW5g5R/E5eXh4nTpwgPj6e9PR0rK2t6datGyEhIXTo0EFqp4gKScIihGg2jEYjKSkpxMXFkZCQQElJCV5eXgQHB+Pv74+Xl5e8GZqBoihkZWWRlJREQkICSUlJqNVqOnXqREhICJ07d5aS+aJakrAIIZolvV7PuXPniIuL49y5c+j1eqytrfHz86N9+/b4+fnh5eUl414agKIoXL9+neTkZFJSUkhOTiYvLw+VSoWfnx8hISEEBgZia2tr7lDFXUQSFiFEs6fX67l06RLJyckkJydz8eJFUwLTrl07UwLTpk0bSWDqQFEUrl27Zvr9pqSkkJ+fj0qlwtvb25QktmvXTmb4iDqThEUI0eLo9XrS09PLJDA6nQ4rK6syCYy3t7ckMBVQFIWrV6+WSVAKCgpQqVS0bdu2TIIia0OJ+iIJixCixdPr9WRkZJjegFNTU9HpdFhaWtKuXTv8/Pxo1aoV7u7uuLm5taiVpY1GI9nZ2Vy/fp1r166RmppKSkoKhYWFqNXqMgmKr6+vJCiiwUjCIoQQv2MwGMokMBcvXqSkpMS038XFxZS8uLu7m76cnZ3vyh4ZRVHIy8vj+vXrpq+srCzTd6PRCIBGoymXoEjhPtFYJGERQohqKIpCfn5+uTfzWz8bDAYA1Go1rq6uZZKYW18ODg5mnaGkKAqFhYVlYr/9Oeh0OgBUKpUpIft9Uubs7CyzrITZ1PT9u+X0fwohxO+oVCocHR1xdHSkffv2ZfYZjUZycnLKJQKnT58mOzub2z/rWVpaYmVlhZWVFdbW1qafK9t2+21LS0v0ej1ardb0VVJSUuZ2Rdtuv307R0dH3N3d8fHxISwszJScuLq6tqhLXqL5kb9eIYSowK1eFVdXVwICAsrs0+v1pvEf+fn5lSYXxcXF5Obmlks2bvXcVESlUlWa4Dg5OVWY9Nja2poSE7mUI5orSViEEKKWLCws8PDwwMPDo073v71HRafTYWFhYUo+LCwspGy9EBWQhEUIIRqZhYUFFhYW2NnZmTsUIe4aMspKCCGEEE2eJCxCCCGEaPIkYRFCCCFEkycJixBCCCGaPElYhBBCCNHkScIihBBCiCZPEhYhhBBCNHmSsAghhBCiyZOERQghhBBNniQsQgghhGjyJGERQgghRJMnCYsQQgghmjxJWIQQQgjR5DWb1ZoVRQEgNzfXzJEIIYQQoqZuvW/feh+vTLNJWPLy8gDw9fU1cyRCCCGEqK28vDycnZ0r3a9Sqktp7hJGo5H09HQcHR1RqVTmDqdJyc3NxdfXl4sXL+Lk5GTucFo8OR9Ni5yPpkfOSdPS0OdDURTy8vLw9vZGra58pEqz6WFRq9X4+PiYO4wmzcnJSV78TYicj6ZFzkfTI+ekaWnI81FVz8otMuhWCCGEEE2eJCxCCCGEaPIkYWkBrK2teeONN7C2tjZ3KAI5H02NnI+mR85J09JUzkezGXQrhBBCiOZLeliEEEII0eRJwiKEEEKIJk8SFiGEEEI0eZKwCCGEEKLJk4SlGdu9ezcqlarCryNHjpja7dy5k4iICBwdHWnTpg3/93//h16vN2PkzVNNz8eRI0cYPnw4Li4uuLq6MmrUKGJiYswXeDNVk/OxaNGiSttcuXLFzM+geanp6wNKz0toaCg2NjZ4eXnx/PPPmynq5q2m56Si/V999VW9xyOzhJoxrVZLVlZWmW2vv/46O3bsIDExEZVKRVxcHH369OGvf/0r06ZN49KlSzz99NOMHTuWDz74wEyRN081OR95eXn4+fkxfvx4XnnlFfR6PW+88Qb79u0jLS0NS0tLM0Xf/NTkfBQVFZGTk1OmzWOPPUZxcTG7d+9uxGibv5qcD4CPPvqIDz/8kPfff59+/fpRXFxMYmIi9913nznCbtZqek5UKhULFy7knnvuMbVzdnbG1ta2fgNSRIuh1WqV1q1bK//4xz9M21599VWld+/eZdqtWbNGsbGxUXJzcxs7xBalovNx5MgRBVBSU1NN2+Li4hRAOX/+vDnCbDEqOh+/d+XKFcXS0lJZvHhxI0bWMlV0PrKyshRbW1tlx44dZoys5arsNQIoa9asafDHl0tCLci6deu4du0ajz32mGlbSUkJNjY2ZdrZ2tpSXFzM0aNHGznClqWi89GlSxc8PDxYsGABWq2WoqIiFixYQFBQEH5+fuYLtgWo6Hz83uLFi7Gzs2Py5MmNF1gLVdH52L59O0ajkUuXLtGtWzd8fHx48MEHuXjxovkCbUGqeo08//zzeHh40KdPH7766iuMRmP9B9DgKZFoMsaMGaOMGTOmzLatW7cqarVaWbp0qaLX65W0tDQlMjJSAZSlS5eaKdKWoaLzoSiKcuLECSUgIEBRq9WKWq1WunbtqqSkpJghwpalsvNxu8DAQOWZZ55ppIhatorOx9y5cxVLS0ulS5cuypYtW5To6Ghl+PDhSpcuXZSSkhIzRdpyVPYaefvtt5WDBw8qx48fVz744APFzs5Oefvtt+v98SVhuQu98cYbClDl15EjR8rc5+LFi4parVZWrVpV7ngffvih4uTkpGg0GsXOzk6ZO3euAigrVqxorKd0V6vP81FYWKj07dtXmTFjhnL48GElOjpauf/++5WgoCClsLCwMZ/WXau+Xx+3HDx4UAGU3377raGfQrNSn+fjnXfeUQBl69atpm1XrlxR1Gq1smXLlkZ5Ps1BQ71Gbvnggw8UJyeneo9bBt3eha5du8a1a9eqbNO+ffsyl3refvttPv30Uy5dulThwE1FUcjIyMDV1ZXk5GQCAwM5fPgwffr0qff4m5v6PB8LFizgtddeIyMjA7W69IqtVqvF1dWVBQsW8NBDDzXMk2hGGuL1ATBr1iyOHTvG8ePH6zXe5q4+z8fChQuZOXMmFy9exMfHx7Td09OTf/7znzz55JP1/wSaoYZ6jdxy4MABIiMjyczMxNPTs15iBrCotyOJRuPh4YGHh0eN2yuKwsKFC5kxY0alf2gqlQpvb28Ali1bhq+vLz179qyXeJu7+jwfhYWFqNVq0+h7wHS7Qa4JN0MN8frIz8/nxx9/ZO7cufUVZotRn+djwIABAJw5c8aUsGRlZXHt2jUZ41ULDfEaud3x48exsbHBxcXlDqKsOBDRzO3YsUMBlISEhAr3//vf/1bi4uKUEydOKP/4xz8US0vLRhnx3VJVdT5OnTqlWFtbK88884ySkJCgnDhxQnnkkUcUZ2dnJT093QzRNn/VvT4URVHmz5+v2NjYKFlZWY0YWctU3fkYP368EhQUpBw4cECJj49X7r33XiUwMFDRarWNHGnLUdU5WbdunfL1118r8fHxyvnz55VvvvlGcXJyUv7whz/UexySsLQAU6dOVSIiIirdP3ToUMXZ2VmxsbFR+vXrp2zatKkRo2t5qjsf27ZtUwYMGKA4Ozsrrq6uyrBhw5To6OhGjLBlqe58KIqihIeHK9OmTWukiFq26s5HTk6OMnPmTMXFxUVxc3NTJk6cWKYMgKh/VZ2TzZs3K927d1ccHBwUOzs7JTg4WPn4448VnU5X73HIGBYhhBBCNHlSh0UIIYQQTZ4kLEIIIYRo8iRhEUIIIUSTJwmLEEIIIZo8SViEEEII0eRJwiKEEEKIJk8SFiGEEEI0eZKwCCGEEKLJk4RFCCGEEE2eJCxCCCGEaPIkYRFCCCFEkycJixBCCCGavP8H/cvu0TNRbo4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 3.38627  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE- do not use clipLines to find outcroppings.  Instead, find boundary counties with low neighborcounts\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d63e9f85-336f-4e38-8ed7-75035e9845f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 772153\n",
      "checking if county 10 with pop 28806.0 and only 1 neighbor is less pop than 193038 vs districtPop 772153\n",
      "checking if county 17 with pop 113777.0 and only 1 neighbor is less pop than 193038 vs districtPop 772153\n",
      "checking if county 0 with pop 68106.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 6 with pop 103725.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 7 with pop 166617.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 20 with pop 154705.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 22 with pop 52460.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 23 with pop 585708.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 10 with pop 28806.0 and 1 or 2 neighbors is less pop than 193038\n",
      "checking if county 17 with pop 113777.0 and 1 or 2 neighbors is less pop than 193038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [0, 10]\n",
      "1   [6, 13, 16]\n",
      "2   [7]\n",
      "3   [20]\n",
      "4   [22, 21, 18]\n",
      "5   [10, 0]\n",
      "6   [17]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 772153.0 193038\n",
      "You may suggest [0, 10, 20], [7, 17, 3], [21, 22, 18, 8, 19, 4], [6]\n",
      "let's decide whether to delete, accept, or modify list [0, 10] with pop 96912.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [0, 10, 20]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [0, 10] with [0, 10, 20]\n",
      "let's decide whether to delete, accept, or modify list [6, 13, 16] with pop 172797.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [6, 13, 16] with [6]\n",
      "let's decide whether to delete, accept, or modify list [7] with pop 166617.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [7, 17, 3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [7] with [7, 17, 3]\n",
      "let's decide whether to delete, accept, or modify list [20] with pop 154705.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [22, 21, 18] with pop 180668.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [22, 21, 18, 8, 19, 4]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [22, 21, 18] with [22, 21, 18, 8, 19, 4]\n",
      "let's decide whether to delete, accept, or modify list [10, 0] with pop 96912.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [17] with pop 113777.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "251617.0 [0, 10, 20]\n",
      "103725.0 [6]\n",
      "373177.0 [7, 17, 3]\n",
      "284018.0 [22, 21, 18, 8, 19, 4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copied 2/17/24 from CO\n",
    "\n",
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [0, 10, 20], [6], [7, 17, 3], [22, 21, 18, 8, 19, 4] \")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15443\n",
      "We defined a total of 0 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "13 fragmented VTDs out of 2042\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hpsg4GzVjQU4uKdPsaBjkpfJEfSn4ehkUn4pTL+5F+5+O4dS/7YMwBWAJuC4fAfflI2LHtnd8ij/+f2Px8e678UXjswgEDqSx55St8vVzOOksAPn4RflwSMeIAU4flagOdJgWPg2EELioRLk5wFVlI1U7ZEnCMb1zv6TRThVjXanP4xlsbkVBuebAoWDmVkqm/KN4HPDMLEHkaAfa/t+DCO1uRdubhyEiFtSxQ7oc++GHNwIATp58E59/vgq799yThh7jkn3VKLswpyZJBATkPJuAatIN+GwKfEnYNPH8PlEHgzoEIpDO/S11y4IsSSh22NCsRzDepcGewPx7qxGFbln4iseJsCVyeqM6pyLDYl1ryjDRM10D7Y53j0Id44OjzBO7LhQ6esn9nLg55X2j3JO77/CDrNWIwmPLrz+nJstJX/U1xqVijFPt/NelYrLHiYluDR1RE25FRshKbGeWgGlitFOFz26L7c6dyy4MrPd1hNCYwXtcUe4TEROBbZ37P7kuHw7PdeWAIqHguvLYMSdPbsIHW68DAFz2tXUAgDMHvo7RY9MT1OTjiHsu4UhNkpgCcSfIZruzkShkScKpSDQlK4m6S3g9v/S7MaTjlBHF6AumkIQQOBQyUGRXMOSiUSNNlhE2LbjzrLKuEAKaLMPkwA2l0wVf9FyXDYc2vhC+OVWxejTt7Z/g493/Ew5HMS6/7EW0ntoCAPjmbf8PXK78qitFyZHQ0MKaNWtQU1MDr9cLr9eL2tpavPHGG7HbW1pacMcdd6C0tBQulwtz585FQ0NDr20+//zzsW3pL7yEw12THlevXo3Ro0dD0zRMmzYN7733XiJdpyQ6HTHRHjVRkIYgrtKpotCuYF9HCAeCYRwIhnEoZKBCc8CwBD4PhtEY0nEoqEO3LLRFzZyecupJu2nBrcicjKK0kiQJvm+NAQDIzs4vHBcW2Dtz9kMAwNe+9jzc7nE40vivsNm8cLlGD35nKSckNFJTXl6OVatWYdy4cQCAF154AfPnz8fOnTtRXV2NBQsWwG6347XXXoPX68Xjjz+OWbNmYe/evXC73T226/V68dlnn3W5TtO+HAF4+eWXcd9992H16tW4+uqr8fTTT2PevHnYu3cvKisrE3kIlARRITBGS1+y7RC77ZIRGQAYrtoxHHZELAEBgeN6BEV2W14tddYUCQeDOiQAFZoDZ6KcfqL0KrimDNqEQtiKu468CGGioeFhAMDu3XfjyitegxE5g7Fjl6ajmxf0K62/ngZIEgPctKioqAiPPvoorr32WkycOBF79uzBlClTAACmaWL48OH45S9/iTvvvLPb+z///PO47777cPbs2R5/x4wZM3D55ZdjzZo1sesmT56MBQsWYOXKlXH31e/3w+fzoa2tDV6vN+77xeNgUL9kd+pctLs9iFLVMSgF86j/opbA/mAYk1mQjzKUEAL/vfM2nD37XwCA6upfY+/ev8cV0/8DBQVT0/a8PRTUu0xvU2aI9/O73+Pypmmirq4OgUAAtbW10PXOb4QXjrAoigKHw4H333+/17Y6OjpQVVWF8vJyfOtb38LOnTtjtxmGgR07dmDOnDld7jNnzhx88MEHvbar6zr8fn+XS6rky8eGW1EY0GSBAyGdAQ1lNEmScPll/4Yp1f8LALB3799DUTx49WdP4vFbbkpjv9L2qykJEg5qdu/eDY/HA1VVsWTJEmzYsAHV1dWYNGkSqqqqsGzZMpw5cwaGYWDVqlVobm5GU1NTj+1NmjQJzz//PDZu3IiXXnoJmqbh6quvjuXitLa2wjRNjBgxosv9RowYgebm5l77unLlSvh8vtiloqIi0YdLFxhozRgaPJos4VTEvOT6Zj2Cg0Edx8JGGnpF1JUkSRg+/Fuxn4uKrsENrsW4efQ/4v/c9+009oyyVcJBzcSJE7Fr1y5s27YNP/zhD7F48WLs3bsXdrsd69evx/79+1FUVASXy4UtW7Zg3rx5UHpJKL3qqqtw++2346tf/SquvfZavPLKK5gwYQJ+85vfdDnu4m+cQog+v4UuW7YMbW1tscuRI0cSfbh0gQNBHaOdjnR3g+JQ5VQh0Jk4/XkwjKBpIWoJdJgmxrhUBMzElsYTpcqFGRClpd+B/Vz9mrH1n6SlPxI6t1eh7JTwPILD4YglCk+fPh0fffQRnnzySTz99NOYNm0adu3ahba2NhiGgeLiYsyYMQPTp0+Pu31ZlnHFFVfERmqGDRsGRVEuGZU5ceLEJaM3F1NVFarKudGBatEjsQ9BTmdkj2KHHcWOzto8x8IGIkLAoyj4PBjGyDyo2UPZQVEUTJ60Cvs+/SmGFl0L6W8VCCEgVmxPS3+ccmc9rHwp0ZFrBrzWVQgRy6c5z+fzobi4GA0NDdi+fTvmz5+fUHu7du1CSUkJgM4gatq0aXj77be7HPf2229j5syZA+1+3olYAsfDRtzl9FuNKCR0FsWb4M693a3zRZnmgAzAIUsY59LgzbO6PZTZSku/g7/8xgFIUufzUpIkyL2smE0llyIjyJHMrJXQSM3y5csxb948VFRUoL29HXV1ddiyZQvq6+sBAK+++iqKi4tRWVmJ3bt3495778WCBQu6JPkuWrQIZWVlsVVLP//5z3HVVVdh/Pjx8Pv9eOqpp7Br1y789re/jd1n6dKlWLhwIaZPn47a2lo888wzaGxsxJIlS5LxN8hpx8MGZEnCSNUOUwhsPu3HeJeGApuCI+HO+i69iQoBTx7WeclFUQEUJWFLC6Jc5lRknErRBruUegm9w7W0tGDhwoVoamqCz+dDTU0N6uvrMXv2bABAU1MTli5dipaWFpSUlGDRokVYsWJFlzYaGxshy19+SJ49exZ33XUXmpub4fP5cNlll+Hdd9/FlVdeGTvm5ptvxqlTp/Dggw+iqakJU6dOxeuvv46qqqqBPPa80BY1UWi34UAwDAkSLvO60B61MMxhw8E4Rms4t5w7LAhELJHQ3llE+UaRJBatzGIDrlOTTVJZpyZTaxsIIdAQ1LudOmoM6QhbAhKA8d3c/sW5fYOqnJn3uChx558LxQ4bCjliQ9SjTH0/z2cpr1ND2WG7P4ihPXyAVTo782SKHZ0jORczBQOaXPJpIAxFAtqjly71JiLKBfy6luOEEH0Wyxtit0GTZezrCGGUU4XzXA5NJH8G8fKCJAFjU7ABKVGu4SLP7MWRmhzX3R5J3dEUGZM9TrQYEbQaUYRNi0+OHDPaqXY7IkdElCv4uZXDDgV1lCZYj2SUU0WHacKEgI1fV3KKP2rCsDj6RkS5i0FNDjMh4OlHPZJRThWNIQMFrGWSM1r0CABgssfZx5FExJn37MWgJkcdCupoPvdB1h+TPU4M48aVOWOYw4ZmPcIl+kSU0xjU5KCIJSBLwDWFBenuCmUIRZIwxePE4ZCBA8EwN7WkuIUjJt7dfzLd3RhUnHnPXvwqnoPssgSmTtDFZEnCmAtqbxwO6SzGR32atKKzYvxzi6ZjVnXv++0RpRtHanKEP2rio7YAjoYNNATC8NmZD0O9q9IcOMrRGurDwqs6K7f/Xd1OvNeQXyM2lH0Y1OQIIQSG2BSc0CMY79a4xw/1SZIkWCwIT324rHIIAGDiyAL8z/+9A0fPBNPboUHA1LPsxaAmRzQbUZSodnBvWUqEKssIcUdi6oU/1Lng4H9cVgbTEnjhg8Pp7RBRLxjU5Ai7JMFjUzDd5053VyiLlGsOHNc5BUU9u+XKSvzFxGKseO0T6FELrjxYFclE4ezFoCbLtegR7GkPQnAagfpJAt/BqWeaXcH/vG5s7Ge3ynw9ylwMarLYp4EQNFmCIcSAatJQfmNATH0ZP9wT+/87Zo5OY08GB3NqslfujyPmqONhA2WqA2ejJi73csqJEqdbFg4GdRQ7EttKg/LPUI+KTx+aC42rKinDMajJUiHLQqnNwa0MqN/8UROlqh0+rpSjOORTQMOcmuzF6acsdCYShVvJnzcYSo1ihx2tkWi6u0FElDQMarKIJQSEEDgTMTEywd23iYgoPhIAk4k1WYnjzlkiYgkcCumwSRJUlrWnJJG58onoEi6ls36Th9P7WYdBTRb4uD0ITZbhtSkcoSEiSjGXIqMjyqAmGzGoyQJuRcZYl5bublCOMYWALlhNmOhiTllGq8V8s2zEnJokSeUeOiyORqnwaSCMiQyWiS4hSxKrN2UpBjVJoqRwDSAT1igVnLIMiWtXibrFt93sxKAmSVLxAmiLRLGnPQinwtNEyWcIC0FuZklEOYQ5NRnsuB5BtccJmd+mKQUmuZ34IqRDkSSUa450d4eIaMA4BJAkyY47mvUIhjvsDGgopaqcKgoUGfs6QjgTiSIQNQEArUYU+wNhhDmSQ3mKb73ZiUFNhgqaFoY6OJBGqeez2zDRrcESQLtp4UAwDN2yMMGt4UBIZ04XEWUNfmomSbLf9/klgQaTLEkXBNFf1kKqdmv4NBBGiWrHEO4RRXmEsXx24khNBrHOvYqEEIjyFUUZQJIkTPY4YVgCB4JhHArqEBc9N89GovgipKeph0REX+JXryQZyPzr6UgUTXoElhBwyDKiQmAC64dQBhmu2jEcdkQsgQMhHYYloMkyTCEwxK5AliSETQsaV+oRURoxqEmS/g6snDQi0C2BKR7nuXYEzkZN2Lm/E2UguyxhXDcBtz9qIsCghnIIE4WzE9+B0uyEEe2ynFaSJBQyd4GyjFuR0WGa6e4GEeU5BjVp1GpE4eDXAcoBiiTBsJgHRrmDaY3ZiUFNGp0wIhjvZu4M5QZZAqIMbIgojRjUpMHhkI4DwTAqWMWVcsgYp4qDXAVFOYKD6NkpoaBmzZo1qKmpgdfrhdfrRW1tLd54443Y7S0tLbjjjjtQWloKl8uFuXPnoqGhodc2n332WVx77bUoLCxEYWEhZs2ahQ8//LDLMQ888AAkSepyGTlyZCJdzxgfnu3AcIcdY10aCmxKurtDlDSSJMEuSRytIaK0SSioKS8vx6pVq7B9+3Zs374d3/jGNzB//nx88sknEEJgwYIFOHjwIF577TXs3LkTVVVVmDVrFgKBQI9tbtmyBbfeeis2b96MrVu3orKyEnPmzMGxY8e6HDdlyhQ0NTXFLrt37+7fI06ReN7G/VETI1U7XFwhQjlqlNOB/cEwOqJMGqbsxpya7CSJiytpJaioqAiPPvoorr32WkycOBF79uzBlClTAACmaWL48OH45S9/iTvvvDOu9kzTRGFhIf75n/8ZixYtAtA5UvMf//Ef2LVr10C6Cr/fD5/Ph7a2Nni93gG1dbFDQR2jXWqvx3zSEYot3SbKZUfCBgBwipWyVjzv6TR44v387veQgWmaqKurQyAQQG1tLXS9cy5d075MfFUUBQ6HA++//37c7QaDQUQiERQVFXW5vqGhAaWlpRg9ejRuueUWHDx4sM+2dF2H3+/vckmXViOKkQ573wcS5YAKzQGfTcHnwTAOhy6tQkxElAoJBzW7d++Gx+OBqqpYsmQJNmzYgOrqakyaNAlVVVVYtmwZzpw5A8MwsGrVKjQ3N6OpqSnu9n/605+irKwMs2bNil03Y8YMrFu3Dm+++SaeffZZNDc3Y+bMmTh16lSvba1cuRI+ny92qaioSPThJoUpBE4aEW5QSXnFa1MwzqWhTHXg00A43d0hSggThbNTwtNPhmGgsbERZ8+exfr16/Hcc8/hnXfeQXV1NXbs2IHvf//7+POf/wxFUTBr1izIcmfc9Prrr/fZ9q9+9SusWrUKW7ZsQU1NTY/HBQIBjB07Fv/wD/+ApUuX9nicruuxESSgc/iqoqJiUKefhBDY0xHCeJfGaquUt9qjJs5GTU5HUdZoDOkoVR2wsbp7Roh3+inhoQOHw4Fx48YBAKZPn46PPvoITz75JJ5++mlMmzYNu3btQltbGwzDQHFxMWbMmIHp06f32e5jjz2GX/ziF9i0aVOvAQ0AuN1ufOUrX+lzZZWqqlDV9M6JftwRwlc8TsgM+ymPFdgUnI2a8EdNeLnqj7KAS1EQtCx4ZT5fs8mAhw6EEF1GQwDA5/OhuLgYDQ0N2L59O+bPn99rG48++igeeugh1NfXxxUA6bqOffv2oaSkZEB9HwxeRWFAQ4TOPJtWI8qVUZQVXIqMALf+yDoJjdQsX74c8+bNQ0VFBdrb21FXV4ctW7agvr4eAPDqq6+iuLgYlZWV2L17N+69914sWLAAc+bMibWxaNEilJWVYeXKlQA6p5xWrFiBf//3f8eoUaPQ3NwMAPB4PPB4PACAH//4x7jppptQWVmJEydO4OGHH4bf78fixYuT8kdIhu7ilmNhg/OyRBcY41JxMKjDw9EaynBOWcIJ1lzKOgkFNS0tLVi4cCGamprg8/lQU1OD+vp6zJ49GwDQ1NSEpUuXoqWlBSUlJVi0aBFWrFjRpY3GxsZYng0ArF69GoZh4Nvf/naX4+6//3488MADAICjR4/i1ltvRWtrK4qLi3HVVVdh27ZtqKqq6s9jHhSGZUG3BMZwSSBRjCUErLiqOhGll8RvpFlpwHVqskkq69QcDukY5ewMYFqNKE4YEUx2a3xhEF3g00AIE118XVB2uPB9ndIrZYnC1DPz3Eonn01BNYvsEXVxQo9ghMPOgIayRv585c8dDGqSRAKwyx/E5V4X37SJutFhWhiT5tWIRJTbGNQkSZVTRRWHKYl6xFCfiFKN1eCIaFBEOZZPWYaD7tmHIzVElFJh00Jj2MBQO99uKLswDs8+fJchopQ5GNRhk4AJbq3vg4mIBohBDRGlxJlIFAU2GcXcnZ6IBglzaogoJc5ETAY0lNWYU5N9GNQQUUrw84CIBhuDGiJKCeZYUrZjonD2YVBDREl30ohgqJ2bVhLR4GJQQ0RJ54+a8HEJNxENMgY1RJR0CjMsKQfwaZx9GNQQUdIxF4FyAZ/H2YdBDRElHT8LiCgdGNQQUdJJACx+zSWiQcaghoiSrkJz4LNAON3dIKI8w6CGiJLOJksY5VSxPxBGix5Jd3eI+sUuSzAsK93doAQwqCGilHAqMia4NaiyhIYsH7XZvXs3HnjgAQSDwXR3hQaRS5ERNBnUZBMGNUSUUkPsNpSqdhwNG+nuSr+tX78eACDLfMvMJy5ZRoBBTVZhdSwiSjm3TUGTkb3TUPfffz/8fj80TUt3V2gQqbIEw2LCezbh1w4iGhRD7Ta0GtF0d6NfhDDQ8PkPsHffP6a7KzSIJFbfyzoMaohoUBTabWiLZmdQ0+b/GG1tO9DU9HscOfI8BJer5w3GNdmFQQ0RDZpsDQVCwS8AAHb7UOxveAinTm1Oc49osDB+zS4Maoho0HgUBR1RM93dSNinn/0MAFBV+X0AQEfH/nR2h4h6wKCGiAbNSNWOk0YUB4M6TmZR4nDxsFkAgM8P/AoAYJqBdHaHiHrA1U9ENKhGu1QAwNlIFAeCYUiQoEhAuebI2N29v/KVf4ZphvGnD76OSOQUVK0k3V0iom4wqCGitBhit2GIvfMtKGoJHA0bsMSleTdhy8IYpwpNSe/AsqJouGL673Gy9Y8YMfxbae0LEXWPQQ0RpZ1NllDlVHu8/fNgGIU2G4Y60vuW5XRWorLi/0prH4ioZ8ypIaKMN86lwRQCDYEwTnAvKSLqAYMaIsoKw1U7xrs1KJKU1VsuEFHqMKghoqwy1GGDIgGnsrQ6MWWXDM1dpx4wqCGirFOiOhAwTRwK6qzuSynFp1d2YaIwEWWlSqeKqCXwSUcIxQ47Rqj2dHeJiNKMIzVElLVssoSpBS60m9lXpZiIki+hoGbNmjWoqamB1+uF1+tFbW0t3njjjdjtLS0tuOOOO1BaWgqXy4W5c+eioaGhz3bXr1+P6upqqKqK6upqbNiw4ZJjVq9ejdGjR0PTNEybNg3vvfdeIl0nohwmg4kPRJRgUFNeXo5Vq1Zh+/bt2L59O77xjW9g/vz5+OSTTyCEwIIFC3Dw4EG89tpr2LlzJ6qqqjBr1iwEAj2XFN+6dStuvvlmLFy4EH/+85+xcOFCfPe738V//dd/xY55+eWXcd999+Gf/umfsHPnTlx77bWYN28eGhsb+//IiSgnCCFgMvGBUoSJwtlFEgPMsisqKsKjjz6Ka6+9FhMnTsSePXswZcoUAIBpmhg+fDh++ctf4s477+z2/jfffDP8fn+XEZ+5c+eisLAQL730EgBgxowZuPzyy7FmzZrYMZMnT8aCBQuwcuXKuPvq9/vh8/nQ1tYGr9fbn4dLRBnmi5COUtUBu8xPH0q+Q0E9trUHpU+8n9/9zqkxTRN1dXUIBAKora2FrusAAE3TYscoigKHw4H333+/x3a2bt2KOXPmdLnuhhtuwAcffAAAMAwDO3bsuOSYOXPmxI7pia7r8Pv9XS5ElFssAQY0RASgH0HN7t274fF4oKoqlixZgg0bNqC6uhqTJk1CVVUVli1bhjNnzsAwDKxatQrNzc1oamrqsb3m5maMGDGiy3UjRoxAc3MzAKC1tRWmafZ6TE9WrlwJn88Xu1RUVCT6cIkow/nsStJ3/I5YAoeCelLbpOzFsgHZI+GgZuLEidi1axe2bduGH/7wh1i8eDH27t0Lu92O9evXY//+/SgqKoLL5cKWLVswb948KIrSa5vSRZOWQohLrovnmIstW7YMbW1tscuRI0cSeKRElA2K7Da0R62ktvl5MAxVltAWYYG/fKfKEnSLQU22SLhOjcPhwLhx4wAA06dPx0cffYQnn3wSTz/9NKZNm4Zdu3ahra0NhmGguLgYM2bMwPTp03tsb+TIkZeMuJw4cSI2MjNs2DAoitLrMT1RVRWqyrlQolxnQcT1RSceR8MGyjQHvDYFnwZC8NqUpLRL2cmlyAhaVtp3iaf4DPgsCSFi+TTn+Xw+FBcXo6GhAdu3b8f8+fN7vH9tbS3efvvtLte99dZbmDlzJoDOIGratGmXHPP222/HjiGi/FalqdjZHsTh0MCmjAJRE6YQ8No6R5fHuzQ0cBoqr7kUGQEzuSOBlDoJjdQsX74c8+bNQ0VFBdrb21FXV4ctW7agvr4eAPDqq6+iuLgYlZWV2L17N+69914sWLCgS5LvokWLUFZWFlu1dO+99+LrX/86fvnLX2L+/Pl47bXXsGnTpi7JxUuXLsXChQsxffp01NbW4plnnkFjYyOWLFmSjL8BEWU5uyxhvEvDSSOK9qiJAlvvU949aQwbmOxxxn5WJAlFdhtOGhEUO1ixOB85ZBlRTj9ljYSCmpaWFixcuBBNTU3w+XyoqalBfX09Zs+eDQBoamrC0qVL0dLSgpKSEixatAgrVqzo0kZjYyNk+csBopkzZ6Kurg4/+9nPsGLFCowdOxYvv/wyZsyYETvm5ptvxqlTp/Dggw+iqakJU6dOxeuvv46qqqqBPHYiyiEFNgUFNgVNuoFjugEZEpRzs0amAArtCobabZB7mEo6GNQxppulu8McNjQEwhhis3GVFVGGG3CdmmzCOjVE+astEsXhsIEaj/OSHJnTkSgiluhx/yghBD4NhLuM4lD+YK2a9Et5nRoiomzis9vwFY/zkhwZSwicNKK9bogpSRLKNAeOhY1Ud5OIBoBBDRHlDVmSYJekLtsqNAR1jI/jW7jXpiAqBALcPJMoYzGoIaK8UuV0oDHUOeLSrEcw3NFzns2l91Vj9yWizMOghojyiixJiAiBsGkhZFootCdWrmu0U2W1YaIMxaCGiPJOmWrH3o5Qv5I/NUWGpkg4w2rDeYO1F7MHgxoiyjtum4LLfe5+379EdaBZj3BPoDzB05w9GNQQEfXDBLeG/ZyGIsooDGqIiPpBkSQMPVdtmIgyA4MaIqJ+Guaw4WzERIRl9IkyAoMaIqIBGOdS8XkwnO5uEBEY1BARDYgkSShV7aw2TJQBGNQQEQ2Qz25DVAgETSvdXSHKawxqiIiSoFR14LjO0RqidGJQQ0SUBAdCYYx1cidnonRiUENENEDHwgZKVQcklp4lSisGNUREAxAwTUSFgNempLsrRHmPQQ0R0QAcCRuo4rRTzuOWGNmBQQ0RUT8dDumo0hjQ5DpNkRBigcWswKCGiKgfzkaiUGUJToVvo7nOrShcrp8l+GokIkqQEALNRgQlqiPdXaFB4JJlBEwz3d2gODCoISJKUENQx3iXlu5u0CCxyRI4+5QdGNQQESXgpBFBoV2BwuXbRBmHQQ0RUZyilsCZiIlihz3dXSGibjCoISKKU0MwjPGuOFc7HXwHePHbwL/OBY7vSmm/iKgTgxoiojh0Vg22x1c1+NPXgf+9AAieAhq3Alv/OeX9IyIGNUREfQqaFiJCwGe39Xnstg+2wHj5DoiJ3wTu3AT85f3A3o1AJDQIPSXKbwxqiIj68EVIx6g4qwaffGMljpqFkP7Hs4CsAMWTAFMH9I4U95KIGNQQEfXicEiPexsE6/BWzJU/wuaCbwEOV+eV7z3W+W8kmKIeEtF5fY+lEhHlqbORKBySBFecVYPl5+dCloC/uffBzivMKHBsB+AsBApKUthTIgI4UkNE1C0hBJr0CEq1xKsGSw535/90tHT+e9OTgI3Vh4lSjUENEVE3Pu9P1eC/eqbz37ZjwIH/D3jxrwHVC5RenvwOEtElOP1ERNQNmyTBJidYNbiqtvPf/1Xd+e+wCcD33waGVCS3c0TULQY1RETdiIp+bPYzpBKY/n1g9++B214ByqYDCt9miQYLX21ERN2w9Xdvp289DnzzMUDm7D7RYEvoVbdmzRrU1NTA6/XC6/WitrYWb7zxRuz2jo4O3HPPPSgvL4fT6cTkyZOxZs2aXtu8/vrrIUnSJZcbb7wxdswDDzxwye0jR45M8KESEQ0SBjREaZHQSE15eTlWrVqFcePGAQBeeOEFzJ8/Hzt37sSUKVPwox/9CJs3b8aLL76IUaNG4a233sLdd9+N0tJSzJ8/v9s2//CHP8AwjNjPp06dwle/+lV85zvf6XLclClTsGnTptjPiqIk0nUiIiLKcQkFNTfddFOXnx955BGsWbMG27Ztw5QpU7B161YsXrwY119/PQDgrrvuwtNPP43t27f3GNQUFRV1+bmurg4ul+uSoMZms3F0hoiIiHrU7zFS0zRRV1eHQCCA2trOjP9rrrkGGzduxLFjxyCEwObNm7F//37ccMMNcbe7du1a3HLLLXC73V2ub2hoQGlpKUaPHo1bbrkFBw8e7G/XiYiIKAclnCi8e/du1NbWIhwOw+PxYMOGDaiu7ly++NRTT+EHP/gBysvLYbPZIMsynnvuOVxzzTVxtf3hhx9iz549WLt2bZfrZ8yYgXXr1mHChAloaWnBww8/jJkzZ+KTTz7B0KFDe2xP13Xouh772e/3J/pwiYiIKEskHNRMnDgRu3btwtmzZ7F+/XosXrwY77zzDqqrq/HUU09h27Zt2LhxI6qqqvDuu+/i7rvvRklJCWbNmtVn22vXrsXUqVNx5ZVXdrl+3rx5sf//yle+gtraWowdOxYvvPACli5d2mN7K1euxM9//vNEHyIREfq7+IlykyQBlhCQ+cTIaJIQ/SnG8KVZs2Zh7NixeOKJJ+Dz+bBhw4YuK5fuvPNOHD16FPX19b22EwwGUVJSggcffBD33ntvn7939uzZGDduXK+rq7obqamoqEBbWxu8Xm8cj46I8tXhBHbmptzXokfgUWS4bVykkg5+vx8+n6/Pz+8B16kRQkDXdUQiEUQiEcgXLWVUFAWWZfXZziuvvAJd13H77bf3eayu69i3bx+uvfbaXo9TVRWqyjclIiIaGLciI2hZcINBTSZLKKhZvnw55s2bh4qKCrS3t6Ourg5btmxBfX09vF4vrrvuOvzkJz+B0+lEVVUV3nnnHaxbtw6PP/54rI1FixahrKwMK1eu7NL22rVrsWDBgm5zZH784x/jpptuQmVlJU6cOIGHH34Yfr8fixcv7ufDJiLq3cDGsCnXOBUZpyLRdHeD+pBQUNPS0oKFCxeiqakJPp8PNTU1qK+vx+zZswF0LsdetmwZbrvtNpw+fRpVVVV45JFHsGTJklgbjY2Nl4zm7N+/H++//z7eeuutbn/v0aNHceutt6K1tRXFxcW46qqrsG3bNlRVVSX6eImI+hS1BBLd9olymyJJsBjoZrwB59Rkk3jn5Igovx0O6ShR7VBZGZgucCioY7SLKQ3pMGg5NUREuWBHWwA+uwIFnUM0DGiIsg+DGiLKe40hHRPcGgq4soUoq/GrCBHlvaEOGzpMM93dIKIBYlBDRHnPrSgImXmTXkiUszj9RER5LWRaOKYbCJh919MioszGoIaI8pY/aqLViGKcS0t3V4goCTj9RER5q0WPYAyX6BLlDAY1RJSXDMuCgxX2iHIKgxoiykvHwhFUaI50d4OIkohBDRHlJQFAljhSQ5RLGNQQUV5yKzI6oqxNQ5RLGNQQUV4a7rDhpMFdl4lyCYMaIspLEqeeiHIOgxoiylusIUyUWxjUEFHeshjWEOUUBjVElLe43RNRbmFQQ0R56VBQR6lqT3c3iCiJGNQQUd45oUfgsysosCnp7gplEVkCTMHhvUzGoIaI8kqTbiBsWSiycz9fSoxLkRHkbu4Zja9qIsppQggcDOmQISFsWRih2lGi8q2PEueSZXSYFkf4Mhhf2USUs0whsK8jhGqPE7IkwRQCCuvTUD85FRknIyzYmMkY1BBRzjoc0jHF44wV2mNAQwMhSxKYUpPZmFNDRDmNlYOJ8geDGiLKSSHTgibzLY4on/AVT0Q56YuwjjLNke5uENEgYlBDRDnneNhAqcqAhijfMKghopwTsix4ueyWKO8wqCGinKNbAodDerq7QUSDjEENEeUUIQSqPU6WsyfKQ6xTQ0Q54XjYQNjqDGSiQnD6iSgPMaghopwQtCyMc2np7kbSRSwBu8xaO0Tx4PQTEWW9Yzm62kkIgX9vOoWzLM1PFBcGNUSU9SJCwKXk3tuZJEn49ohC/OlsB/YHwghxh2iiXuXeuwAR5Z1czgl22xRcX1QAmyRxRRdRHxjUEFFWO6FHcj7nxK0oOBo2MNxhx7Gwke7uEGWshIKaNWvWoKamBl6vF16vF7W1tXjjjTdit3d0dOCee+5BeXk5nE4nJk+ejDVr1vTa5vPPPw9Jki65hMPhLsetXr0ao0ePhqZpmDZtGt57771Euk5EOarDtFCeB9shVGgOBC0LRXau7yDqSUKvjvLycqxatQrjxo0DALzwwguYP38+du7ciSlTpuBHP/oRNm/ejBdffBGjRo3CW2+9hbvvvhulpaWYP39+j+16vV589tlnXa7TtC9XMbz88su47777sHr1alx99dV4+umnMW/ePOzduxeVlZWJPAQiyiFCiLypRyNJgG5ZcOZg7lA2yY9nW/ZK6NVx00034Zvf/CYmTJiACRMm4JFHHoHH48G2bdsAAFu3bsXixYtx/fXXY9SoUbjrrrvw1a9+Fdu3b++1XUmSMHLkyC6XCz3++OP4/ve/jzvvvBOTJ0/GE088gYqKij5HgYgotx3TI6hy5v4oDQA06RHIyO1ptmzAM5DZ+h3ym6aJuro6BAIB1NbWAgCuueYabNy4EceOHYMQAps3b8b+/ftxww039NpWR0cHqqqqUF5ejm9961vYuXNn7DbDMLBjxw7MmTOny33mzJmDDz74oNd2dV2H3+/vciGi3BGxBBxybo9cGJaFE3oE29sCUPiJStSrhN8Ndu/eDY/HA1VVsWTJEmzYsAHV1dUAgKeeegrV1dUoLy+Hw+HA3LlzsXr1alxzzTU9tjdp0iQ8//zz2LhxI1566SVomoarr74aDQ0NAIDW1laYpokRI0Z0ud+IESPQ3Nzca19XrlwJn88Xu1RUVCT6cIkog6V6KqBZj8Cw0ruM+oVjp/DB2Q4sLhvGqQ+iPiQc1EycOBG7du3Ctm3b8MMf/hCLFy/G3r17AXQGNdu2bcPGjRuxY8cO/PrXv8bdd9+NTZs29djeVVddhdtvvx1f/epXce211+KVV17BhAkT8Jvf/KbLcZLU9SuKEOKS6y62bNkytLW1xS5HjhxJ9OESUR47E4nicCi9q41uLSlCqWrH0bCBJj2CIGvVpJUkAVae5HFlo4TT6B0ORyxRePr06fjoo4/w5JNP4oknnsDy5cuxYcMG3HjjjQCAmpoa7Nq1C4899hhmzZoVV/uyLOOKK66IjdQMGzYMiqJcMipz4sSJS0ZvLqaqKlRVTfQhElEWMCwr5Uu5bZIEWx9fnlLNY1Nw5RAPDgV1jCpQ0aQbGJuD20FkC02WEbIsuBXuLZaJBjwZLYSAruuIRCKIRCKQL5rfVhQFVgLDt0II7Nq1CyUlJQA6g6hp06bh7bff7nLc22+/jZkzZw60+0SUpdqjFrwpXAl0IBhGyLIwxJ7+D68PznTghBFBY5jF99LNpcgcLctgCY3ULF++HPPmzUNFRQXa29tRV1eHLVu2oL6+Hl6vF9dddx1+8pOfwOl0oqqqCu+88w7WrVuHxx9/PNbGokWLUFZWhpUrVwIAfv7zn+Oqq67C+PHj4ff78dRTT2HXrl347W9/G7vP0qVLsXDhQkyfPh21tbV45pln0NjYiCVLliTpz0BE2abIrmB3Rwg1KajbIoSAABA2LRRmQF2YmYUeNOsRqLKEViOKM5FoRvQrH7lkGWe4F1fGSuhV0dLSgoULF6KpqQk+nw81NTWor6/H7NmzAQB1dXVYtmwZbrvtNpw+fRpVVVV45JFHugQfjY2NXUZzzp49i7vuugvNzc3w+Xy47LLL8O677+LKK6+MHXPzzTfj1KlTePDBB9HU1ISpU6fi9ddfR1VV1UAfPxFlKUmS4EnRFEBrJIoRDntGLd8dqdphCoGTRhRnIyaDmjSxyRIsptRkLEmI/Ml48vv98Pl8aGtrg9frTXd3iGiAGgJhjHcnP78kagl8FgxDlSWMy6D8FUsIfNIRQrHDjpGqPd3dyVuHgjpGu5ivOZji/fxmqE9EWStVObyGEPDZlIzbFduwOncjPx2JYpjdBluO73mVqQKm2ePmom5FRoGiQGPl57RgUENEWcsuSTAsK+4CfFvPdqBAkTG1wAUA6Iia2OkP4tqigtgxR8IG2qMmqj1OfBHSoVsW1Awp8KcpMkwBTHZr+DQQxmSPM91dykvnnz8XE0IgYFo4FYnC0LufBBHoWpVYki7dZf78dRcG7UIAigTY5c4VeTIkyFJnW7IkQT7XriRJ564DZHz5/+dvy3UMaogoa9klCWYCE+hFdhvebm2DP2phuGqDXZLguGi044uQjiE2BbplocqppmyKq79KVTv2BcKo0Bw4GjbyYjPPbCFJEjw2BR5b8nO9hBCICiAiBKJCQAgBC4Ap0PkzBCzRGTRZ5xLdLXxZUycZeUAXB2QXC5gmhjvsGJ7GqVEGNUSUtXRLQE1gCma4w4aZQzxojURRJtmhWwJfuehb99Xnbm+LmBiuysi0GR6PTYEsAR5FRpMeiasQKWU/SZJgl4DMSl/vqjGkpz2BPTPGVImI+qHK6Uio4m+h3YbLfW78RZEXpugsrue6KPehLWpCt0Ts22axw46TRiSp/R6oITYb2qImxrpUHOwht4NosJkCKS+I2RcGNUSUteR+jlCcf+ONXpTMEDIttEaiXaZ0vDYFbVGz/51MgZGqHSeMKAxLxKYbiIhBDRFlOdGPbR4bQzpKVDsUqTNXAejcduFgSO9xCXemVb+Y4NbQGDYwxsnRGsoMmTALyqCGiLJW0OzfyiSHLMMSonN1yLl34gNBHdU9JARXag4cCad3Y8vulKp2fBYIo8huw76OEPwZNqJE+SUT4n4GNUSUtZr0/q3+CZgm3BesUDkeNlChOXpMuHXIckKrrAZLgU2BS5FhCWCyx4kWPQI9gb32iHINgxoiylrD7LZ+JfGeD1DO59QELavPZbgZGNMAAKqcKsKWhTORKMa7NRwOGWjj3kSUpxjUEFHW8tltaI8mPjJhOzci41RkHAx25tf0pT+5O4OlXHPghBFF1BKY6NYQMC20GgxsaHBlwiuEQQ0R5R3r3NtvuebAGJcKdxwbY4502HE8A/NqzpvgUvFpIAQAKNUcsfyasGnBzIRkB6JBwKCGiPLK0bCBEY7EK566bQqCGZyvIkkSKp2dgY1hWdBkCYeCOlojUTQEw+nuHuW4QNS8pOZTOrCiMBFltQKbjPaoiYI4StOfjkQhgLiO7Y7cSzXXz4Nh6JZAxBLw2Drf3L2KMqgl4702BV6bEweDnUu8XYqMkaodZyJRnDQiKO5HMEcUjxNGFKOc6d+yI/1hFRHRABQ77Diu950sfEKPIGhaqBjAXkm9leFo1iOY7NbwNa8L41waxrk0RITAF2moITPGpWKMS4VdkrA/EIYmyzjF5GFKIYHM2DCTQQ0RZb1Cm9LrKqiTRgQmxIA3f+wtM2XmEA8OhXTsaQ/GrivTHFAkKW31Y4Y6bPBHTTTpEYxxqtjXEUpLPyj3pT+c6cSghoiy3nDVjqgQaOxmVORo2IAQQIma2qFxWZIw1qVhjEvD58FwbISmXHPguG6kLVl3us+NViOCU5EoyjK0iCBlv0xJRWdQQ0Q5oUR1wK0oXVYoNYZ02CUpaXktqiz1WdzOpcgY59Lgsyn4LBDG6UgUE10aPg+mbyuDK4d40BG14LUp0C0r47Z8oOzHkRoioiQb6rBBkjqTdj8PhjHUbsOIJCbqlqh2HAvHV+xviN2GiW4NrUYUkiTBluZ3/XLNgc8CYRTbbTiUwM7mRNmEq5+IKKekcpqpP7uCnx8T6W3l1GBwKjImujV8FgjDngEJnZQ7LCEyYjNLgCM1REQJSfS9O5MqEf9HyxkICEjo/CAiSoajYQNlKc5ZixeDGiKiBJgQcQcELXoEQ2yZMyA+yaPh4/YQSrX4lsETxcMUgF3OjKEaBjVERAk6ENTj2kizwzQxchCL7/VGCIFPO8IYpTlgicxJ7KTsl0ljfgxqiIgSMM6lYbxbQ9C0cKKP0Y4L82gCppnWVUeSJOFM1IRNlrC9LYDSDAm2iJIpc8ZFiYiySJVTxdlIFA2BMMa7tW6PiQgBIQQkScIktxONYQNVTnWQe/qlia7O1ViWABrDBio1R59VYJt0A7p1aTBWZLfB28/tJih3hE0LaoZMPQEMaoiI+m2I3QZZknAkbHS7/cJop4pPA2FM9jhhkyV0ExsMqtohbpyNmmgxIugwLRwI6Rjn6j4ga4tE0WJEUaba4VYvDV4OBXUGNYQmPZIRez6dx+knIqIBOF/Qrjt2WYLXpiBodt5upjn7QJIkFNptmOR2wiFJ3S4zt4TA/kAYIUtggluDm4EL9SET9nw6j0ENEdEAlaoOHOth+wFNlhE+F/RIQMZU85UkQJGAwAX7Uh0PGzgY0jHepfaZ4JwZj4LSLdOeB5x+IiIaIEXq+c3dJgHGuUBmpGpHsxFJ+T5U8Rjn0rA/EEZj2IAmyzCEQKlqhxcKmvQI2qImNFnGaFf6coAo82VSHSaAIzVERAN20oii2NH9d0SvTUGrEQUA+KMm3ErmTOeosoSxLhWjXSomujWosoTDIR1ORcZkjxMjVTs+6Qh1W5eHBfwobFpwyJkVRmRWb4iIspBhCag9vLlLkgTt3G0lqgPNGVT0rlJz4MAFG20eCOqY4nGi0N4ZoDkVGR5FhtlN7FLOHb/zXmMPCfLpxKCGiGiAJHRuotlTQT6PTY7dZpekAY1wBEwTG1rOJCU3R5IkVDodOBAM40AwfMkSbyEEwpbotlqsXZa6DXYoP5wyovBlYBI5gxoiogEa7VIxzqXBEuh29KLYYUd71Dr3/zbs8Af7Hdi4FQUVmgP/ebINO9oCAw5u3IqCsS4NY12XrnQ6GNIxJo11dShznYlGMSIDCzgyqCEiSpIRqh0Fioz9gXCPx3hsCi73utA4gKmbmgInJns0WAAOh1IzBRS1BBRJ6nVPH48io/2C1VOUP9K963xPGNQQESXRELsNVU4H9naEEDK/rF8jIGKjKmcj5oAK1zlkGUHTQoEtdauTjoQNVPWRLzFctePkuSRoyh8n9EiPifHpllBQs2bNGtTU1MDr9cLr9aK2thZvvPFG7PaOjg7cc889KC8vh9PpxOTJk7FmzZpe23z22Wdx7bXXorCwEIWFhZg1axY+/PDDLsc88MADkCSpy2XkyJGJdJ2IaNCosoxqjxPHdCM2kiHwZZGys9Eoiuz9+1AImxb2doRQpjngkFL3vdSlyDjLURjqRodpoSAD82mABIOa8vJyrFq1Ctu3b8f27dvxjW98A/Pnz8cnn3wCAPjRj36E+vp6vPjii9i3bx9+9KMf4W//9m/x2muv9djmli1bcOutt2Lz5s3YunUrKisrMWfOHBw7dqzLcVOmTEFTU1Pssnv37n48XCKiwTPOpaFJjyBsWl2G66V+DN2fr1r8aSCMQruCzwNhfBoIJa2vFzsVicZWQfUlUwoKEiX0VeGmm27q8vMjjzyCNWvWYNu2bZgyZQq2bt2KxYsX4/rrrwcA3HXXXXj66aexfft2zJ8/v9s2/+3f/q3Lz88++yx+//vf449//CMWLVr0ZUdtNo7OEFHWmeDWsLcj1GXTv0RCmi9COkzROX0VEZ1bFxwLG5juc+NgSO+7gX5yxll/pFS145geQXmGLe2l/NTvsUvTNFFXV4dAIIDa2loAwDXXXIONGzfi2LFjEEJg8+bN2L9/P2644Ya42w0Gg4hEIigqKupyfUNDA0pLSzF69GjccsstOHjwYJ9t6boOv9/f5UJENNjGXbTtgKZIOBjUcSjYd1BiCmCMS8VYl4ZJbidcigxZAmRJ6nEzysGkKTKMdO/USYPmlBFFkT0zp56AfgQ1u3fvhsfjgaqqWLJkCTZs2IDq6moAwFNPPYXq6mqUl5fD4XBg7ty5WL16Na655pq42//pT3+KsrIyzJo1K3bdjBkzsG7dOrz55pt49tln0dzcjJkzZ+LUqVO9trVy5Ur4fL7YpaKiItGHS0Q0YA5Z7lJJuER1oFJzICJEj7VtzmsIdq6k2h8I48/tQTQEwhjWz3ycRHFaiS52NhrFkEF6/vWHJBJ81hqGgcbGRpw9exbr16/Hc889h3feeQfV1dV47LHH8Oyzz+Kxxx5DVVUV3n33XSxbtgwbNmzoEqT05Fe/+hVWrVqFLVu2oKampsfjAoEAxo4di3/4h3/A0qVLezxO13Xo+pffhPx+PyoqKtDW1gav15vIwyYiSokDwTDG9jLiciAYhl2SMNRhG9QtFsKmhdZINK5ppeNhA4V2G5wKF9Tmur6er6ni9/vh8/n6/PxOOKi52KxZszB27Fg88cQT8Pl82LBhA2688cbY7XfeeSeOHj2K+vr6Xtt57LHH8PDDD2PTpk2YPn16n7939uzZGDduXJ+rqy4U7x+FiGiwNATCGO/+8kPiUFCHLAHD7DZ8ETYw3GFHe9SEQ5bgtSmDuurkUFCPa8m4EAKHQgbGcPPLnKZbFlr0CCrTUJAx3s/vAYfVQgjouo5IJIJIJAL5ouQyRVFgWVYP9+706KOP4qGHHkJ9fX1cAY2u69i3bx9KSkoG1HcionQT+HKa53QkCp9dQZnqQFvUxHiXhmEOG4Y6bCi02xA0LRwI9lzYL10u3FqBctfRDNzr6WIJTYwtX74c8+bNQ0VFBdrb21FXV4ctW7agvr4eXq8X1113HX7yk5/A6XSiqqoK77zzDtatW4fHH3881saiRYtQVlaGlStXAuicclqxYgX+/d//HaNGjUJzczMAwOPxwOPxAAB+/OMf46abbkJlZSVOnDiBhx9+GH6/H4sXL07W34GIKC0qNAcOnFvFpFsCUzxOAEDpBR8e5wv1uRQZnqiMrWc7MMJhhykEyjQHXJz2oUGS6QFsQkFNS0sLFi5ciKamJvh8PtTU1KC+vh6zZ88GANTV1WHZsmW47bbbcPr0aVRVVeGRRx7BkiVLYm00NjZ2Gc1ZvXo1DMPAt7/97S6/6/7778cDDzwAADh69ChuvfVWtLa2ori4GFdddRW2bduGqqqq/j5uIqKM4FRkjHNpEKJzyXZf3DYFtUM8sZ+PhA0cC1sYYldQ7Ih/L54/twehyhImuZ29HmcJATmOD7IRDhuadAMlamZ/k6f+6099pcE24JyabMKcGiLKVaeMKNqiZtx5LfsD4dhH1Cin2u0eT6YQ+CKBXJmDQZ15NTnqdCQKIYChadoeYdByaoiIKP2GOmwYaldwNM6NMsvPLSkfYlfwf06e7fYYRZJgCtHvHcUpd5yJRNMW0CSCQQ0RUY7w2W1QpM5Rm764FBkT3Ro+D+r4WoGrx+PGulQcirNysU0CjD4WhlB2yoapJ4BBDRFRTilRHTgbjXbZIbwniiRhrFPFn9uD2B8I43g3ozyyJMES8RXiq9AcOBruvZggUSoxqCEiyjFjXRoOxzm6YgiBMS4VxQ4bAj0EQlVOB76IY1or01fGUP/4oyYKbNkRLmRHL4mIKCGVTke3Iy8XK9ccGOVU8UlHCKVa96unHLIMk3k1eavViCa0si6dGNQQEeUgt6JAj3OjySY9gtohHrT2kotjkyRE42hvmMPW535WRKnCoIaIKEeF40jatYSABOC4HkFhLxsVVmoOHIxjSstrU9AeZbIwpQeDGiKiHKWeK3S6pz2ID8504Fg301GfBcIY51JhWFascnF3JEmCJksImGbK+kuZ53zQmy0Y1BAR5agCmwx/1ESZ5sDeQAh/OtuBXf5gl2PsstS5/1Qc7VU61bhWN8kSmIOTI47pEZT1kGuViRjUEBHlqGKHHa1GFIV2Gyo1B0ZpDhwK6fi4/cvAZrRTxd6OEOQ4v48X2RWcifReB6dCc+BInEUAKbNFLQGHnD2hQvb0lIiIEibOjcH8RZEXx/UIhjtsmOJxoj1qYn8gjANBHVM9zrj2nQK+DJR6o5yrbUPZL9tOI4MaIqIcdv5DyS5LaDc7p6JebjqNgyEdumVhgluDJEmIJjBdpEhSXMX4iAYbgxoiohxWbLeh1Yii1YhizlAfThpRTHRrGOdUETSt2LYGrgSmGCo0Bxr7mF4qsMlo62OaijJbxBJQsilLGAxqiIhyms9uw6eBEIDOBF5NljBSteNPZzsgATgU6gxOLh53efd0O3ZelFR8nl2W+pyuKnbYcSrClVLZ7LhuoFxzpLsbCcn8LTeJiGhALitwwX1uufb5yrBFdhu+COuwn9vawCFLCJkWnErnd11DCFT28IHWGNJRombPihjqH1N0TjVmEwY1REQ5zt1N/RmnImOY3Q6n3PmhVa45cCAYRqHdhqBpwRKi22XZQgiELQG30nNNm4uP555QNFg4/URElKeGOWxdAh4JEj4LhPGHljOwBLpNHv4ibGCMU42r/TLNjqM6t0ygwcOghoiIAAAWBC4rcOGaIR7sag+ioJsRHksANjm+kRdVlhHh2u6spFsWHHGe50zCoIaIiAAAZaoDB0I6DCFwW+lQuBV+ROSr4+EIyrIwb4rPWCIiAtCZZ6NIwFiXiv2BMILmpRtTFie4C7d2LgGZsosAsjIXikENERHFTHI7Ueyw4y+HenGim8rBBTYFHQnswl2i2tHEvBoaJAxqiIioW7pldVs5OJEsmWz8tp/vhBCx7TWyDYMaIiLq1gS3hoagfsn1DFNy2zE9gjI1u4runceghoiIuqVIEobYFJwe4HYHwx02nOAUVNbQLQtaliaJZ2eviYhoUAxX7Th70XYHiU5MeGwKAkwWpkHAoIaIiFKOqTXZ4ZQRRaE9ezcbYFBDRES9Ui9alt1dpeHeCCGQ4F0oTc5GoyhiUENERLmqVLXjSNiI/WyXpG5XRfUkaFos5EeDgs8yIiLqlSRJ8NoUNATCOBI2UKrZcSyBxN9Tkeye0sgXzXoEIx3ZV0X4QnyWERFRn0aqdhTaFQgBHAjq0OT4vxNHBWDPwn2E8k3ANDEyC7dGuBBHaoiIKC6qLENTZBTaFegWVzPlkkSmEzMZgxoiIkrISIcdzQlMP3GMJvMdCRso17Kz4N6FGNQQEVFCJEnC1YWeuI/PjTGA3BYRAmoCU4qZijk1RESUMEcOfADmg46oiZNGtNc6QUIABYoyeJ1KIQY1RESUUpx+Si0hBFojUbRftHu6BMCtyBjldOTNxqIJhdpr1qxBTU0NvF4vvF4vamtr8cYbb8Ru7+jowD333IPy8nI4nU5MnjwZa9as6bPd9evXo7q6Gqqqorq6Ghs2bLjkmNWrV2P06NHQNA3Tpk3De++9l0jXiYiIspolBI6GDRwM6jgY1HHo/CVkwCFJGONSu1xGu1QMV+15E9AACY7UlJeXY9WqVRg3bhwA4IUXXsD8+fOxc+dOTJkyBT/60Y+wefNmvPjiixg1ahTeeust3H333SgtLcX8+fO7bXPr1q24+eab8dBDD+Gv/uqvsGHDBnz3u9/F+++/jxkzZgAAXn75Zdx3331YvXo1rr76ajz99NOYN28e9u7di8rKygH+CYiIiDKHbllo0iOwziUjnQ9JZKlzaX0u5L6kiiQGuI6rqKgIjz76KL7//e9j6tSpuPnmm7FixYrY7dOmTcM3v/lNPPTQQ93e/+abb4bf7+8y4jN37lwUFhbipZdeAgDMmDEDl19+eZdRn8mTJ2PBggVYuXJl3H31+/3w+Xxoa2uD1+tN9KESEVGCDMtCixFFRQ6srEk2f9TEKePSHdDtsoRS1Q45j0ZY+hLv53e/wz3TNFFXV4dAIIDa2loAwDXXXIONGzfi2LFjEEJg8+bN2L9/P2644YYe29m6dSvmzJnT5bobbrgBH3zwAQDAMAzs2LHjkmPmzJkTO6Ynuq7D7/d3uRAR0eA5aUQx3JG/6ZtRS6AxpHeZMjoc6vxXtyyMcjow+txU0flLueZgQNNPCT/Tdu/ejdraWoTDYXg8HmzYsAHV1dUAgKeeego/+MEPUF5eDpvNBlmW8dxzz+Gaa67psb3m5maMGDGiy3UjRoxAc3MzAKC1tRWmafZ6TE9WrlyJn//854k+RCIiShLDyo2lwvE6FNS7/CxLQKnqYEXlQZJwUDNx4kTs2rULZ8+exfr167F48WK88847qK6uxlNPPYVt27Zh48aNqKqqwrvvvou7774bJSUlmDVrVo9tXpzEJIS45Lp4jrnYsmXLsHTp0tjPfr8fFRUV8T5UIiKiuDXpBrw2BUPzeGQq3RL+yzscjlii8PTp0/HRRx/hySefxBNPPIHly5djw4YNuPHGGwEANTU12LVrFx577LEeg5qRI0deMuJy4sSJ2MjMsGHDoChKr8f0RFVVqKqa6EMkIiJKWMC0UOJi7lA6DXhMUAgBXdcRiUQQiUQgXzTMqCgKrF72CKmtrcXbb7/d5bq33noLM2fOBNAZRE2bNu2SY95+++3YMUREROn0RUiHP2qmuxt5L6GRmuXLl2PevHmoqKhAe3s76urqsGXLFtTX18Pr9eK6667DT37yEzidTlRVVeGdd97BunXr8Pjjj8faWLRoEcrKymKrlu699158/etfxy9/+UvMnz8fr732GjZt2oT3338/dp+lS5di4cKFmD59Ompra/HMM8+gsbERS5YsSdKfgYiIqP8iQmCUkzMD6ZZQUNPS0oKFCxeiqakJPp8PNTU1qK+vx+zZswEAdXV1WLZsGW677TacPn0aVVVVeOSRR7oEH42NjV1Gc2bOnIm6ujr87Gc/w4oVKzB27Fi8/PLLsRo1QOey71OnTuHBBx9EU1MTpk6ditdffx1VVVUDffxEREQDctKIYLjDDq8tN7YayGYDrlOTTVinhohocB0O6Tk/gnEwqGOMK7cfY7qlvE4NERFRX3Lta/PpSPSSgnlcrJ05uO6MiIhSwrCsAdVnEUKgIahDkQC5l9DBLksoS+EeR2HTwlHdgASgyG4DJODzYBgSgDE5PgqVbRjUEBFRSpw0oijuZ82WE3oEZ6ImxrlUKH0EK7pl4XDIAICk1ok5aUTQFjWhyjLGubQutxXZbbCEwGfBcM6NRmUzBjVERJQS/akmfD5AGWa3YaJb6/sOAFRZxmiXioBpoj3acwmReFlC4NNAGCWq/ZJg5kKyJGGS2zng30fJw6CGiIhSItHZoMaQDlMg7mDmYieNKCqTsHHm/mAYk91ayqazKHUY1BARUVq1R00cDRuodDrgVvq/LFoIDHgjyCNhA2WqgwFNlmJQQ0REafNFSIcsSZjsSf80jj9qQgJQwHozWYtBDRERDbqgaeGLkD7g0ZlksYTAcd1gjkyWY1BDRESD6mjYQFSIQRudCZoWTkWiiFpdlymdn2ESAugwTUzJgNEiGhgGNURENCgMy8KBoI4yzZGSLQXORKNAEDgfukjo/H9NljDCYYNdkpgrk+MY1BARUco16xEETDOlozOXe90pa5uyA7dJICKipOuImnArMkwh8GkgBIcsYWwvNV+IkoEjNURElHSnIlE4ZRmfB3VMdLHmCw0OBjVERJR0LqVzIqC/hfSI+oNBDRERJV2xw57uLlAeYk4NERER5QQGNURERJQTGNQQERFRTmBQQ0RERDmBQQ0RERHlBAY1RERElBMY1BAREVFOYFBDREREOYFBDREREeUEBjVERESUExjUEBERUU5gUENEREQ5gUENERER5QQGNURERJQTbOnuwGASQgAA/H5/mntCRERE8Tr/uX3+c7wneRXUtLe3AwAqKirS3BMiIiJKVHt7O3w+X4+3S6KvsCeHWJaF48ePo6CgAJIkpbs7GcXv96OiogJHjhyB1+tNd3fyHs9HZuH5yDw8J5kl1edDCIH29naUlpZClnvOnMmrkRpZllFeXp7ubmQ0r9fLN4gMwvORWXg+Mg/PSWZJ5fnobYTmPCYKExERUU5gUENEREQ5gUENAQBUVcX9998PVVXT3RUCz0em4fnIPDwnmSVTzkdeJQoTERFR7uJIDREREeUEBjVERESUExjUEBERUU5gUENEREQ5gUFNntuyZQskSer28tFHH8WO++Mf/4iZM2eioKAAJSUl+Md//EdEo9E09jw3xXs+PvroI/zlX/4lhgwZgsLCQsyZMwe7du1KX8dzVDzn4/nnn+/xmBMnTqT5EeSWeF8fQOd5qampgaZpGDlyJO6555409Tp3xXs+urv9X/7lX1LSJ65+ynOGYeD06dNdrluxYgU2bdqEgwcPQpIkfPzxx7jiiivwT//0T/je976HY8eOYcmSJbjxxhvx2GOPpannuSme89He3o6qqirMnz8fP/3pTxGNRnH//ffjvffew9GjR2G329PU+9wTz/kIhUJoa2vrcswdd9yBcDiMLVu2DGJvc1885wMAHn/8cfz617/Go48+ihkzZiAcDuPgwYO46aab0tHtnBXv+ZAkCb/73e8wd+7c2HE+nw9OpzP5nRJEFzAMQwwfPlw8+OCDseuWLVsmpk+f3uW4DRs2CE3ThN/vH+wu5pXuzsdHH30kAIjGxsbYdR9//LEAID7//PN0dDNvdHc+LnbixAlht9vFunXrBrFn+am783H69GnhdDrFpk2b0tiz/NTT6wOA2LBhw6D0gdNP1MXGjRvR2tqKO+64I3adruvQNK3LcU6nE+FwGDt27BjkHuaX7s7HxIkTMWzYMKxduxaGYSAUCmHt2rWYMmUKqqqq0tfZPNDd+bjYunXr4HK58O1vf3vwOpanujsfb7/9NizLwrFjxzB58mSUl5fju9/9Lo4cOZK+juaJ3l4f99xzD4YNG4YrrrgC//Iv/wLLslLSBwY11MXatWtxww03oKKiInbdDTfcgA8++AAvvfQSTNPEsWPH8PDDDwMAmpqa0tXVvNDd+SgoKMCWLVvw4osvwul0wuPx4M0338Trr78Omy2v9qgddN2dj4v967/+K773ve+lZmiduujufBw8eBCWZeEXv/gFnnjiCfz+97/H6dOnMXv2bBiGkcbe5r6eXh8PPfQQXn31VWzatAm33HIL/v7v/x6/+MUvUtOJQRkPokF3//33CwC9Xj766KMu9zly5IiQZVn8/ve/v6S9X//618Lr9QpFUYTL5RIrV64UAMTLL788WA8pqyXzfASDQXHllVeKRYsWiQ8//FBs3bpV/PVf/7WYMmWKCAaDg/mwslayXx/nffDBBwKA2L59e6ofQk5J5vl45JFHBADx5ptvxq47ceKEkGVZ1NfXD8rjyXapen2c99hjjwmv15uSvjNROEe1traitbW112NGjRrVZVrpoYcewm9+8xscO3as22RTIQSamppQWFiIw4cPo7q6Gh9++CGuuOKKpPc/1yTzfKxduxbLly9HU1MTZLlzsNUwDBQWFmLt2rW45ZZbUvMgckgqXh8A8P3vfx///d//jZ07dya1v7kumefjd7/7Hf7mb/4GR44cQXl5eez6ESNG4OGHH8YPfvCD5D+AHJOq18d5f/rTn3DNNdegubkZI0aMSEqfz+NYdY4aNmwYhg0bFvfxQgj87ne/w6JFi3p8QkqShNLSUgDASy+9hIqKClx++eVJ6W+uS+b5CAaDkGU5trIAQOznVM1T55pUvD46OjrwyiuvYOXKlcnqZt5I5vm4+uqrAQCfffZZLKg5ffo0WltbmXMWp1S8Pi60c+dOaJqGIUOGDKCXPXeGSGzatEkAEHv37u329l/96lfi448/Fnv27BEPPvigsNvtg5bNno96Ox/79u0TqqqKH/7wh2Lv3r1iz5494vbbbxc+n08cP348Db3NfX29PoQQ4rnnnhOaponTp08PYs/yU1/nY/78+WLKlCniT3/6k9i9e7f41re+Jaqrq4VhGIPc0/zQ2/nYuHGjeOaZZ8Tu3bvF559/Lp599lnh9XrF3/3d36WkLwxqSAghxK233ipmzpzZ4+1/8Rd/IXw+n9A0TcyYMUO8/vrrg9i7/NPX+XjrrbfE1VdfLXw+nygsLBTf+MY3xNatWwexh/mlr/MhhBC1tbXie9/73iD1KL/1dT7a2trE3/zN34ghQ4aIoqIi8Vd/9VddSiBQcvV2Pt544w3xta99TXg8HuFyucTUqVPFE088ISKRSEr6wpwaIiIiyglc0k1EREQ5gUENERER5QQGNURERJQTGNQQERFRTmBQQ0RERDmBQQ0RERHlBAY1RERElBMY1BAREVFOYFBDREREOYFBDREREeUEBjVERESUExjUEBERUU74/wEMDMgTpy1n7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 13 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1000\n",
      "looking for surrounds, now working on vtd 1501\n",
      "looking for surrounds, now working on vtd 2002\n",
      "After surround checks, we appear to have 1650 building blocks defined. We captured 19 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "working on unit neighbor lists for county 20\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "            \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, the piece we selected is surrounded.  KISS - take on this neighbor's neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e79873ab-0b8e-48a9-b4b0-05b3135b9cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 71\n",
      "border is discontig if we skip 1648\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{596} is the list of 1-neighbor 'border' units that can be eliminated from the border set\n",
      "... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to remove these from the list of border units 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success! we removed {596} from the list of borderUnits\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/MD2042convexHD2_8nW4_21Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\n",
      "I found 91 populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\n",
      "  (too many to plot individually)\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 1 - we already have an ensemble of HDpoly's and their weights\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"pigs\" == \"can fly\": #countyNo in shapeDF.columns.values:\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "if len(unassignedList) > 0:\n",
    "    if len(unassignedList) > 20:\n",
    "        print(\"  (too many to plot individually)\")\n",
    "    else:\n",
    "        print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "        for t in unassignedList:\n",
    "            print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "            plotPoly(hdCP[t].buffer(0.1))\n",
    "            plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "            plotPoly(countyGeom[HDcountyNo[t]])\n",
    "            plotPoly(MAP,0.2)\n",
    "            plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2fb6d6e3-6157-4728-a6e5-ea22ede3de03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIONAL - write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"24Mar24\"\n",
    "print(\"OPTIONAL - write the unit topology to a file\")   #New for FL 2/17/24 - include parent VTDs\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c47fa1ff-2bed-4641-96e9-8dbdb7cb6da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxCCBfrac (e.g. 1.0) prior to discontig shedding 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cluster no 0 containing counties [0, 10, 20]\n",
      "halfway there! last use was 0.96403\n",
      "our final fractional capture area to normalize this cluster's usage is 0.67415 from 245 intersecting HDs\n",
      "working on cluster no 1 containing counties [6]\n",
      "halfway there! last use was 0.95258\n",
      "our final fractional capture area to normalize this cluster's usage is 0.31104 from 251 intersecting HDs\n",
      "working on cluster no 2 containing counties [7, 17, 3]\n",
      "halfway there! last use was 0.87941\n",
      "our final fractional capture area to normalize this cluster's usage is 0.25151 from 269 intersecting HDs\n",
      "working on cluster no 3 containing counties [22, 21, 18, 8, 19, 4]\n",
      "halfway there! last use was 0.87979\n",
      "our final fractional capture area to normalize this cluster's usage is 0.25019 from 296 intersecting HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing w/option 1 - set the cluster area thresholds to unitize cluster use\n",
    "#NOTE -- THIS MAY NOT BE RIGHT -- DEPENDS ON HOW WE GENERATED THE HDpoly's\n",
    "print(\"default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\")\n",
    "maxCCBfrac = float(input(\"enter maxCCBfrac (e.g. 1.0) prior to discontig shedding\"))\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    nIntersections = 0\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < maxCCBfrac - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #maxCCBfrac vs. 1.0\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t].intersection(MAP.buffer(1)),0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]),\"from\",nIntersections,\"intersecting HDs\" )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t].intersection(MAP.buffer(1)),1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aad5e7db-93db-43d8-8196-dd6dd02f3014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\n"
     ]
    }
   ],
   "source": [
    "#print(\"I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\")\n",
    "#CCBareaFrac = [0.17, 0.25, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "217527a2-0acf-43ec-9a25-09e0aecce76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\n",
      "We'll later add nearby or jettison faraway contiguous units until we approach the aDP\n",
      "  the HD pops and lists already appropriately include captured corner county clusters\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HDno 0\n",
      "working on HDno 504\n",
      "working on HDno 1026\n",
      "working on HDno 1534\n",
      "all done assigning units to HDs based on original HD polygons\n",
      "Here is a scatterplot of active HD pop excess vs relative to target 772153.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, compute the current unit usage, plot its histogram weighted by unit pop\n",
      "unit avg and sd usage are 0.94907 0.09178\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity ...\n",
      "CCB cluster 0 with pop 251617.0 originally had use of 0.9946681551453243\n",
      "CCB cluster 1 with pop 103725.0 originally had use of 0.9970122501627678\n",
      "CCB cluster 2 with pop 373177.0 originally had use of 0.9981311993865596\n",
      "CCB cluster 3 with pop 284018.0 originally had use of 0.999720262693957\n"
     ]
    }
   ],
   "source": [
    "#continuing w/option 1 - now we determine 12-wedge-capture (frag) vtd lists for the original HD polygons\n",
    "# FOR MD, THIS IS VERY SLOW\n",
    "print(\"This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\")\n",
    "print(\"We'll later add nearby or jettison faraway contiguous units until we approach the aDP\")\n",
    "print(\"  the HD pops and lists already appropriately include captured corner county clusters\")\n",
    "#see above block for preliminaries\n",
    "nonUnitCounties = list (set([c for c in range(nCounties)]).difference(set(unitCounties)) )\n",
    "areaFrac = [0.5 for u in range(nUnits)]  #default; we will accept unit counties if we capture half their area\n",
    "alter = input(\"enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage\")\n",
    "if alter == 1:\n",
    "    for u in range(nUnits):\n",
    "        if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "            CCBno = int(allUnits[u] )\n",
    "            areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "            print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "        CCBno = int(allUnits[u] )\n",
    "        areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "        #print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "# for c in unitCounties:   #UNCOMMENT TO ENFORCE UNIT COUNTIES TO EQUAL USAGE\n",
    "#    areaFrac[allUnits.index(c+0.5)] = unitCareaFrac[ unitCounties.index(c) ]\n",
    "\n",
    "origHDpop, origHDlist = [0. for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "for jj,t in enumerate(popHDlist):\n",
    "    if jj%500 == 0:\n",
    "        print(\"working on HDno\",t)\n",
    "    hC = HDcountyNo[t]\n",
    "    #if hC in allFusedCounties:  #using a swelling technique as original HDpoly's look skewy\n",
    "    if STATE != \"MD\" and hC in allFusedCounties:\n",
    "        origHDpop[t], origHDlist[t] = clusterSwell(hdCP[t], CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, aDP)\n",
    "    else:\n",
    "        if hC in unitCounties:\n",
    "            iCP, iClist = countyPop[hC], [allUnits.index(hC+0.5) ] #automatically include the home county if small\n",
    "        else: #must probe in-county list vs HDpoly intersxn\n",
    "            iCP, iClist =  getPolyPop(HDpoly[t], unitCP, unitPop, countyUnitList[hC])\n",
    "        nonCP, nonClist = getNonHCunits(HDpoly[t], hC, allUnits, unitCP, unitPop, allFusedCounties, areaFrac, countyGeom,\n",
    "                                        neighborCountyList, countyUnitList)  #this will miss county clusters; see below\n",
    "        origHDpop[t]  += iCP + nonCP\n",
    "        origHDlist[t] += iClist + nonClist\n",
    "        for j, geo in enumerate(CCBgeom):  #special for corner clusters -- intersection area must clear threshold estbd in a prev block\n",
    "            unitNo = allUnits.index(j+0.25)\n",
    "            if geo.intersection(HDpoly[t]).area >= areaFrac[unitNo] * CCBarea[j]:\n",
    "                origHDpop[t] += unitPop[unitNo]\n",
    "                origHDlist[t].append(unitNo)\n",
    "print(\"all done assigning units to HDs based on original HD polygons\")\n",
    "HDpopExcess = list()\n",
    "for t in popHDlist:\n",
    "    HDpopExcess.append(origHDpop[t] - aDP)\n",
    "print(\"Here is a scatterplot of active HD pop excess vs relative to target\",aDP)\n",
    "plt.scatter(popHDlist, HDpopExcess)\n",
    "plt.axhline(-0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.axhline( 0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, compute the current unit usage, plot its histogram weighted by unit pop\")\n",
    "origUnitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in origHDlist[t]:\n",
    "        origUnitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(origUnitUse,bins=50,weights=unitPop)\n",
    "origUnitUseAvg, origUnitUseSD = getWeightedAvgAndSD(origUnitUse,unitPop)\n",
    "print(\"unit avg and sd usage are\",r5(origUnitUseAvg), r5(origUnitUseSD) )\n",
    "plt.show()\n",
    "print(\"prior to correcting for discontiguity ...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",origUnitUse[u])\n",
    "\n",
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0fdec9f3-be08-4952-9745-bb0e47e5a04d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "470 441815.0\n",
      "1502 387743.0\n",
      "1770 387743.0\n",
      "1772 387743.0\n",
      "1773 387743.0\n",
      "1774 387743.0\n",
      "1775 387743.0\n",
      "1776 387743.0\n",
      "1777 387743.0\n",
      "1779 387743.0\n",
      "1780 387743.0\n",
      "1781 387743.0\n",
      "1783 387743.0\n",
      "1946 284018.0\n",
      "1948 284018.0\n",
      "1951 284018.0\n",
      "1954 284018.0\n",
      "1958 284018.0\n",
      "1959 284018.0\n",
      "1972 284018.0\n",
      "1989 284018.0\n",
      "1990 284018.0\n",
      "2003 387743.0\n",
      "2004 284018.0\n"
     ]
    }
   ],
   "source": [
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 450000:\n",
    "        print(t,HDvPop[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70a8d159-68bb-4436-8a07-0ea73d3f5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#END OF OPTION 1 -- SKIP AHEAD TO AFTER OPTION 2\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "15507d95-4f47-463d-a46f-380a9195cba6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy() #reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e74fe279-1089-4774-8b1f-2bdc8d30887d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, we move in partial counties before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state MD\n",
      "clipPoly 0 intersects [0, 9, 10, 20] counties and a total of 132 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state MD\n",
      "clipPoly 1 intersects [3, 4, 7, 8, 15, 17, 18, 19, 21, 22] counties and a total of 235 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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XyrvIjVvQtQhYK8s4auwcKlIrhm2nKAq1q7pY8Wo9ipwcawmCgCAKiKJAQohxT/G1JCQH/bkPYPbXc9antTx1/ElYo258OgcAD//3eaZZT0JXbAMZtFkmUs4qO+D+qxyeqILlcESUQI102S/8gVps1iri8b1HhuyNSCKCTho914coiIRiUTTi0IhQKwk09QXw+occN7+Tlz5i37d7PXx/25BDcKXFyKIZ5V+4v18Uj2c9mzZfQyzmQqt1IggiHR0vsL321xgMWWRnn0te7rcQxS8v02ooFOKRRx6htzeZV+TJxx/jvLPOJOhx0VG3HXtGFtll5Sy6+hJ22CZi8Uxgxjnn0fzQf5g0JoOdp1xE19hcFlxxAa1TlmPqnE6hK2lZUQywoXUx7p89x4X3/RFt3AcGO2iS1/TnJ1XwxqYObstZwznex5nQ9SDP1ZhpvPsUbnxhJUtdmykOruG+pQ8xp2AOc+fO5bxf3TGs/wuPqSce9zMzsB1ZnobVMg6t1kFGdpigR8vY45pJZAXZuSxIWkxhjC4TgPiSVjr7wtTP7+V3Z2/k1DGnck5qKlt6tzA2ZezgfSeJAredNI78LAufPFFDXU0XXqKkZAbpWLeeV31RzrzjCF6p7eOht3qpMZ3FGWVFmJY8ybmxJXT5rh0ULLGYmx07fo8sR9DrMxkz5id7vJZ90ThHfPAfdL7JHLumgcqog+AyN0+e/Ry/OflXRCIRTANh2x88UU3N8k7S8i0UV6WhKCDLCj2N6+ioXcKE+T+CECS0SaEZsJTgNSSrqn8mVgBqzC6mkRQ7kSYP8Z6gKli+gaiC5XBFb4WwFwy25LSQnEgKmUMQJRpFOMDMowmvFzkUQpuZ+cX6oCSIRntBEEgkgkiS6YDbave3U2wvHnWdKxin1+OiKmfIjyIvxUQ0LmPQpAEtAHzQ5+UYp23YvjuCYVK1EmvmTODhlm7+1NhJTFbQigfPHC7LcWrr7kCnS2fmjNfQ6zNRFJmOjv8Sjfbj91dTV3cHwWAjFeW//tKO29zcTG9vL+eddx5d7c0sXbYSXySGM68AndGIPSMLn28bq46Zy5gtEqVaAdnjoWXxUQQ+kFlVdhW/K7yPlzaks3ViEw/NOJ2nt/2Huefdzu9+sYBjdReSpStm7X8eYFbzHSy32Dnikjcgu4q8FBPjs20s7dRwavFU8MOkfAfRlhaOfjyBbeoZKCKE3CE+2PEB+fn5GBIprHytgeO+OwGzPWkV02gsOOzThn0vh81AV4cfry/Axm1rMOW5qJXiGDrTKRJTSCgKHo3IUXlH8f6575NlzuK9xve4cUkyr8uH53+ISWPCpDURTSiYtBJTTyigbnUXAN07V6HE63DV5bFqZQdz5+Ty8PjLB49/R9Np3NJQyq0DPjAbujdgSnTQ1PESd/JrfsCfycw8Dat1/Ihr8vHHH1Nfv4WCeacQth5LvNXNYnsq5xx9E/VvlWHdcSr33389wWAOP/3pz1j+4k7WrtlO2lyRjCo9LaGt9Ab7cYX7aTJtRrHV8Jj/24iigClYjeSqx2stxFVo4vb6v/H7ou8SErUgSJyoORFkSLtyIv3/qUYO77kKtsrXE9Xp9nDFmgn+7uTf9nzwtB7c/uyG4Nq1xLp7CNfU7DH/iSLLRBsbiTY1EevqItrURKShATkYBEEg3tMzbPtoUxPRxsZ9rheT4phNLO5BFDR4vBsP+PuEQm3EYsOrAO/6vTyhEJNyMxBFAd74Cdxuh0W/RqcRSbemDG731+buEW1n6bS4YskokJl2M3EFGkOREdv9r0gkwmzYcDle7ybGFP+IrRELLeEogiCSk3M+hYXfQ6tL+uGYzaUAhMNhQqEvFoL9yiuv8OKLLwKQkmIGzc3YbN0sXryY9MJi7BlZAGi1Di7bUEmX6KFe6sS3cAotBg3/sVmpylzFa/E5bOqPM1+j4xedT2BsLKFF20ZFux9fzVOs7n2HyuwYDVoN30u3s+3fx0DLKgDuPKuSRfI0ptZfCcAx9jg7jjue6vESFnsPWu3QNXc4HGz7pJ22Wjfr3tl7yHwkHOStt94BoKoqzJgJH7DItI5X3FHaj8hEmpkU91nm5Pf81+Z/De674PkFzPrPLHpDvYPua2l5Vr73wDwmHJWDpBuLZDgGo1VLdLuH+s/dPz//9nn8X/6ZKL9fQ90Ty7n07Uv503038daKE6lTymknD7+/ekSf136ymMWLF5GZ9Veq3/klt+8MMT7axVVj3wBAjp+PQ7uWSZPfJUXuQX9XOuG2+cTLfsz59ZfT9/55/Kb6F/y1+Y+81PoCcmMXV3zgYGLvRE5rP4G4mECIfMzzt/yAHVnFXNP6PEe51vLe9Umx5dEmndkFUSDWGUSbceADDpXDF9XCcjjz2RNL0oJ8aEaRaJxOdHm5KPE4kbo6DGPHAqAkEijxOLLPR8LrRZAktPlJZ0ElEkH8nEUl2tSEHA4TbWpC0OnQZmcj6HREm5qQrFY0aaMnDhvsh8ZMimMGAMFg4z71PRzpJBEPYDQWADKBQAOdITcFjkoA+p94ku777kMJBil57110BQWIgjDkILjmEV6ymKla+QClpQsRi+ZynkPDC+44+lGsJnMcFhTgw34fsxxJJ9PtgTBlZsM+9ffLYOu2G/H7qnGkzMbjWUsgUM/UKU+RkjKLu2uaWekJ8MmsZNG+UKiR1tbHycu9lDTnufT09KDX61EUBVmWBx1l94dgMMiGDRsAkLKyefT9D5mZk0ABOjo6ePDBB5k1axYFBQWkp+dgmuLjmA2VpJ9fQvrYcwj+4XjSwuOZ9Opq2mekQImOvA8+Zeb4k0iY7sD0l5uJzjoO44nXsXDqHLR6A2LTm5zua6c0GoNtr0L+TKYUpPDvK2bw7tZObjy+nE2nn8THF14AwOQJHxONTWLd2hLOOuUCaj7qY8uS9xA1edSt1XHUBWP3+B1zCx2Df4eCVbS0xNFqtVx1/1z0ej1b6+uGbV/dX43Dr/DPl9K54rweAkaBmz76GdePuxNItiUhML3UTpq/iiXrewn5YpisOmwaEU8sjl2bfNRLgoDQ5GOL1Ep73TZOzgpwwWqR5ng1xqK/UZW1gV/tOJ7rzAGm2oau39LF7wAmolEjsYiJ35YbyHzfyuOWy3E+lo7G0UAo5KFpZxVnyEkx1qjVsMRk5JZ+F+f6ArxmPIr8prmkhZIZiZfPEhnfLFKmE+j3v0GvZSv+rGPo0KVzXcUvmfnKGrSJBAvWLeamvBd5uO3n6O57kHhvJTFbD6D68H3TUAWLyldHIk68bSdKLIZgMKDLyyNcW4ug0YAgIBoMiEYj+jFjhu0mmEaOnnSFhUR27kRXWIhoGHqB64uLCW+vRXI4ku3uA4IgoSgygjC6gdEXbOLpziCLPSJpOj2/L2pHKwpYrePoCC3HoLOiRKN0/f73RNKK0AXriTa3oCsoQPnMxXZx0lHyfWcOb0Y8PLLuSSiaywKrxAvuOB/2j/SlKTbpqTAbeLfPw6kZDlK1EtsDYU7dp2/15dDZ+crg3xqtg6lTnsRun0I0GqVcA8dm2ejr6yMcbqKn52kAtLrJxGIx0tOTfjk7gmEe3tGKT9Jwb0UBJmnfDbkmkwlp5pH0bd6Aubsbv8HI8sZzUJRkG93d3bz++utYLBZuuukmUi8oJz5LS29zI1nmscz/9RqOaGqi/tUTKcufxxE8iJEIbPvnoD25Ir0J5iwYOubVH3PnjkXJnEZFcweXL6jIYEFFcmrv43nJrLU2s5nunnw62tM48cQT2fqWF1dHADlagxyrx3fkDP61uYYTCk8g35Y/6nd0OoamAqurPdjtU/nud89Br09OJRn0elxuLykOGy/VvQRAoTYTpa2dbxd+m792P8na7tVYp3uJtlnpe3wrCW8yWZwDOG9+DvqFBVidRkRRoDYQHhQsgijgvG4i3vvX0SnF+Fl7nPCcmXxSejW/OHM+sz7ZTKDax5vxOhbPKKdMA09edR25CSNCaiEvV5dyVc12Htn6ClfPOx7tFjf9NoXCYDX3Z9Tx3UAxM6Y+ha7FQ5GmmwUtNdw+8Dsb0yOAsQ+PcXhOodXA3OIC0rOb2Bg8F+0mFy+VLuDneXU8VHwhs+onMFeewobAdqzd69BPnkDfg78h7coTEER1kuCbhCpYDmckHcQjoDk0IklG0F+PqaIAnCXIwSDR1lZ0RUUIGs0BPWj0xcWseauReDRB+ews/O4IYX8MjTYF7YrtZIzLQedM2Ws7CrsXK/9o6WZpr48P17tQDBJytom/jp+ENEpYZRwNy8b9hImJh7G98ALGiZWDUULyjCsRP/4jF3a3UBaLQeezcPpfGGcYOm5vNFnNd1dy9Fo8seT8fLnZQG1wqObNVxHeGQ0FETVaNNqkk2Ve3qX09LxPTu5FaCQL0Wgf9fWvodMZuDi7nHisg76+JTS3PIIoGsnPu4KiwpMRRR2uWJxxn2wBktpABl7rdvPnigIuyE7d5z41ZORRfXQuj2db6NObOLu6hbn+HqZtXUskkpziKC0tHdw+NSePvpYmvD3d2NIz0BUWMq5mYFrjr4ugtxbl5D+R0Bejefls6P1cPSlRhLHH77FPxcXF7Ny5E28ggLd+BjabjXBmmI/5iEqORtJPRdRk8pb7aVrWVfPY1sdYesHSwWKHu9LW5OFHN/yMWMRLTI6Rmz1c2JTkFbC1vo4Uh43bPr0NgCZblKt+qMHT/STTMqfx47TvY/pTN90kpxYDuijR+Q4ytopEN/RgzLYgzks6smbrtbSHo+QYklNNxnwHE8+cRPdjKwnc8AYFlVX8EHi6vQ+fO4auJYBilOgeG6b55UeZkXEGikbC076O69zVdPUeR1PRPM5efw3EcpmknEKe/UimeG9mYvcavrP2eSq3V9NaXkBaLIA7NQWvzYYiiuRk5jFj5nSMz/4Hz0cfESlPEJ8fo23FqdTrvk+d0YzUGSKRbeR+wxSOtlczkDCYoLkcTb4O2beTouceV8XKNxBVsBzO2HLB3QipAxYKRdmnvCzxeJyOjg60Wi1GoxGbLTnii4SCyPE4Rqvti78Yg/2gt0E0AIBoMmEYO5bIzp2IZjPajJHJvfaFla81ALB2FF8Bq6OfonILY+YUklcx+gsyHvcjCqNHQNRt2MytrqRY0ATjSO1BotmmUcVK36OPIsox0qxhzP52fO9uIu26a0noZBobG3nssceAGziu/HnaWm10hrKZ+rnIi55obIRg6YzEmGFPmuLHmgys9CTPn1ZrJx73otXad39y9pOg18NDV10CgDUtHaPFhrXAj6XMQ+3234Awun+QJFnIzbmIkpKfIklD1i6LNOT0/T2/hDee4GkH/Ku1Z78Ey7JgjEucZp7Z5GNDSwuMN/OJNYMXfv5zNmzYQGdnJwsXLgRg54a1pBUUIul0bP7gXTKKS8gqHTtUWyiSLAS4zZnPey1L+TFA1sR9P0kDnHTSSTz44IODn6cdM40fLP0B+lwTlR1HI+lKKT7GwjnjTuC+ddXkWnIHxevn8QYjxLwxbE4roeiekz6+e867nPDiCbgjbn545I0cVzSfQlshPQ9uJMpQCv6ehToWffg6t/78V7Tfvpzg+m6sA4LFqpHYsmkjO2o24cjMpnTGbMbNnc+4ufOHHevG7S3gTA5+Lql/laefbeHv37ueN+9+hm1SK3MmaajPTce1PEHWUTcRfjXB1B1NdE5opt+9lV+8CWDnEvvHNFYsRB9Oo2LdP5EFgXBGJtsuOJ+WkI/n336TVKOJmZKWaHEm9TsuJKFkY3bKdCh9mMpTuXT9y8T988nrqkBBYWtWNcdmOVjjG4fxyBy2O1NI9LhJKBBXFGRALwpMtBgpMB6iAziVL4wqWA5nRHEovNmWA952sO85i6vb7SYWi5Gbm4soikQiEVwuF6FgEH9fD4WlZbg62pPVb7NzRk19vitKLIag/ZwAUBTwd0HGOOgbqp/S3eRFZ84k3u+i7cNX0OTYyR9XiS1tZIjv7rj49lmsf7+Z6mUDD3oB8spTyBpjp722mc0rXWxe6WLh5RVUzBlZvdfvr8FiGT1UOH7h+Zx46bUsnTqTCaEGtk6fxOwUy7Bt9N11sG0p3udeQlRkql6/EX1FBdl3/B19WRlCU/WAWEl2rimUS1toPFbreKZ+bkS4YPV2OhdMHrZM3EUclZsN/Kejn5isoNHYCYVavlTBoh+YepM0GsYfdQxhv5eQ10v/8gx6mhrQW01cdOedSBqQ5RjBYAM6XRoWy7hRk+9pRYFbS3L4TX07gahCXZcPHFZ+MSZ7n/v0t+ZuvAkZXyxOkVXP/LFprCDpxCsIAlOmTCERjyMNTP/pTWa83d04c/MZO+tIFFnG3b1LJugJZ8GKv7E10MajTS8lBUvnZnC3gGP0KZvRkGIRZmSmkjX9CNILS/nlmusBiGiDXPPHWdTNnUvhtY/z0+5ksrq7jrprt6I/0Bnmg+eSNcFScnWI56aSP250QZdjyeHjc9fS2hukchffl/RrJxHa0ENgbReRHW4KtZmcMHk+7bcvB0CbN3TfduzYzqe/vxWdaCQqh9i6ZDEX/eYPI471wqQS6kMR3utKcGb1MqLuVXSumEO6bKNSyafN8TaJhoWYznyX+/gZvzzjl7yc9iTPk7yPLBd/C1kQSWh1JESRBALKlQtGHGeQBWdiCyb40etJJ/bFFiNZLamc8JEEnEizPsq6Ah3vTDWTkI7kpc/2i7hhs3u3zV6a4yQiyxhEkUBCxhdPsDMUQRIExluM/Lo0h3Sdlpis0BaJUmDQDfvdqRy6qILl64LWCPHwHjfp6+vDZDLhcDgGl+n1evR6PXUdbZRPmgKAwWxBURS21NRgLhxDPCEjSQJWUSDDMHz0EmloAEEgYM7BbNdjsGjZUP0Krz36ByquvIHz06fj7Q2hN2vRGTQ4Mkw0tlez6KV/Ydem0WgqIZgfYuIxJzB29lxEaQ8C6dXrSKl+nWMW3sYxl36XvnY/21d0Ure6i+attUS9T/LtPz/CB080s+y57ZRPSxsWTq0oCrG4B51u9w66l7+7mhktEzjmoz+S9sPrST/x2uSKD+6ApffQbdCji8UpOlKmc40d8znXYPvuzQiiSDQuoxFFrrjiCt555x3C4TB1tUegKAIpKUM/tTl2M8s9AaqsI5OWRWR5MIx5rNlATFFoDEUGHG+/3Nw7kkbLpONOYvvyT5hy4qmYHcnpNDkcp3HHVl7+7S9p29LA2NlJvw6TqXCvbR45IPA25GjxZ6YixmKDDsShUIhgMBntodVqBy17u/Lb+mRl5KdcIU5qj3FLXZR0q8hL+VqUhIIgCVR//CHOvALS8gvx9fWi0elIyUkKdUEUScnaRagakgLvlMX3Mv7SZ+GvRyaXN3wIUy/b53NlT88kIzcXR1EF+WlmNvRsAOCVM15BEAUEvZ4wMZa0JrPOxgK9EApC5oQRVk+tMvTZ1RblnX9u4so/zRsmcPRaHaFwGKPBwPZWL7PKh9+zgiBgmpKBcVI6XfeuJf5aF/lICHoJ6/x8rPOT1pVwwM9r996FTjJwVsEPaR7TzvLFT7Lx738j3+Ig5eKLBqdWjkq1chRWLrvQyfd/dQKb5WKuXd/H8SfNYJJJSw3jcXsuZTMV1AkVSE6ReQVhnm9OCpYKq5l5O2uRW1pQQmEURYMhEUJSZKREAsv0aTgWHoOoQGtTE3I8hkgEOQVwQXlDGGSBVinBdkOcI50xXsu7E1uXzEXrf8ECS4i8K49An2ZEIwiIgoBGSDoSiwLc39jFX5q7eb3bTYlJT1RWMEsiZkliss2EhMCznf281OUafm01EpuOnIBenWI65FEFy2HK6s7V/HX9Xzk352hOSx2zzyn6jcbkS1JRFKLRXiTJRDgQw2QyE21NOoJqcy0IgkBCp2PZuibyV9+Bd9qPiPq7mNuYRYO2h9nfPg5BEhD1erzYePbXi4mFPmH+1Rfyf8v/j3veSPDy5EVsnTSBiOQjXa/DmWuhqS/AK/ckHVLLS46gODKO57b8nuYtm/D29jDzjHNH73iwn9j2t9lAmBlv/gTGnogzJ5cjzi5lzlklPPnLRfjJwZpqJ7sshYAnSqy9HV1R0WATiUQQjWbkS/Kz8wGQ6qqmYnvSmVT/2b6fPgBL7wHgqfxyuuQYz+7cQc5sN8wqSVq6gIb+LopSMsi0pNDZmRzlH31kA76Ai0AwadXJS82mytPJciA4SrbbsWYDS/p9PPpJA86BtPE1X2Gk0BHnf4u6VctZ/MhDnPaTX0BcoePOlZhOzUZvMrP2zVfJHFOGPWPf8uBMspp4a1oZV21ppC0W44c5qYTdbsKAwWDA6UyGQUciEXp7e9HpdMOES8e8SdQvbiSwqQtnTwyXTqAokOD+dXHa1n2C45KxxGMxQj4v/e2tpObk4swroGH9GkRJZMyUGcM7NOFM+Oh3mD0tVDYsH1pecsy+naCwF3RmNrz3Jjnl4wmTFAtXTbyKf27+J3esuIM1XWu49C/fYoo96Wxx+fjLKdc7wVEI/Q3DMlKvf6+ZT1/aQen0DArnZNC92c2WpW1Ub9xKYXkRZmNS8I3Jy6dmZwPjS0rR63b/IhVEAcdpY+j991ZEs4bMn0xHMg9ZPOvXrMTf14skaIjpY2xZ+iYAff/+NzpPANFkwnH2WcPaFEWBhcdfjunFWtyuXhaHElx06yymkYmibGN852vM8K0kJX4mR+VWMCk7lbkra1iTENBMnMo5xy1kgsU4LMpoBJlDvmaJo2VqH93K81s6Sa98iVbZxHXNp3JD7s/QxGBq6/EURWTKJxaQkbd7C+MtJTl8Lz8D58A0ayghs84bYIU7wHK3n7XewKj7nZBmQ6daWA4LVMFymPJx68es617Huu51zMuZiy19zxlRg/4AmlDyxex2r2LN+m/xV37MZTxCZfofyItVsujFV6lI5JLz7Um8291D6Jnf8nfbsSwzvMov3slivq6H3sgFvKdfge72OJN/MgfR6mDlXauREy7kaDXLnupm4umzuO3P4xgvZXPK003AcH+Ty8uPwVz/CY3BGjxSL3nlE/B2d+MYyK8xKhueZo0Q5ersTFY2tmDytqHYchAEgQ2+EJG8NHSRb6HR6elvD5CSbUaORok2NYGioMTjhNp3IE0cfcosHG4n5x8P0PXL2ynUtFCw6H1wprLm9ZfoePUpYvIE9AY9R1h2MCEahqmXQulxUH7SYBvl6Vm8Vb+N9ISOU04/gzdfexVFEdFIVjo6OkgkElhNNnqiyfOxI5h0IG0LR8kdcIi8Mi+ds9bv4IP2AF2rApQdmcFT7X2cmv7lTQXtislmZ+F3ruH1P99N7YpPGDvzSBSTREp2GmfdfBuv33c3/7r+uwDY0jOoWngiM884F0EUaQpFeL/PyzSbmSm2ociuqTYzK2ePpzoQYpzZOCzr72d8ZtkLhUJ0d3fjcDjQ6XR4FzdjWNyKAbCfXkLmrCw+cfuJv9yIZocH99O15F49jmgsDIJAam4+oiRROn3W6Dl50sth5vdg1cPw1k3JZbnTk0VE94S/G96+Gba+RKjq20w+9Q80uqOMcSZfwgsLFvLPzf9kTVey+OGTNU/xZM1TzMubx+UTLoeQF1CSuegHaNjQw6cv7WDaSYVEhQYcZgf5Jxay+aNW1r3kw3mNh96uzuRuCIMRZ5rPjfwTssL5Dy9nbZOLv148hVOrcsi940gQkynvh11fuyO5jxLnpZp70UkaKrvMRGyzwfchppmfE3iAIivwaS/j2ISkb6a//SwCnghmux5BEEhJOxWnEKAg1YRBp6EUaJ5XxZs9Hv7Z2sNPtydzQp2SbueUdAcnpdkx7iFS7KlfLcfvipCGRJWvkJXhFG4yNGMTZbyyyNFHXE3Op71Y5u35mnlicTb5gqzwBFjh9rPeGySqKNg1ErPsZn5WnM0su5kik54UjaTWJzoMUQXLYcp1U67jqeqniMkx3mlZxPmfCZZRHG/jfSGa/raCpkgnJZNK6C24jTB6VgpHME1ZxSxrOj1/r2aVYQcSIjlp02m5t4mI280UpZO/e44mXZ7PFvff6UptpdKZzxrJyNt1HeQs/wimv8F/uI3vbZ7NTkc/WqPA0sgC6lZ0IZlrufboiby3Tsv2Lh+pQhDEIMHi8WQecQTHH3/8QLcVetdU421owzYmFyWRQNh1eui9XzFGkjg77MCoKODr4MGWnsEphKlSmFMCcR689kMAbGkGDGMnD+6uKArheDeSbvioT1EUAoFaDIY8ElMLGPvxJwB0RKI8c/m5KIpMumkKOl2EnX09zM6cRv4Z58HkS0Zck7s3ruFhj45wyw7sGg0zjjqBvvUrcPrdgI/qzRuorJzId1I1vOROOvfOXL6N5nCUS0wi1rdfomrSBGbmTWRVSoC/L6girhO5bPNOnuvs5/SvRrMwdvZcxs6ey+JHHiK1qATpogzWrnwDs8PBxXf8kW1LP2TZc0/i7enmk2efoOrYk/h1h4fH2noH2/h2bhq/K8sdfAloRIGJ1r0n9zIajRiNxsHpSuu8PHyLmzEfnQ3lOjSigHVnLdlnjafnnmSRwegTHWhOdyIaxEFfFmD3USMn3pUULJ9RdcHuOxT2wPqnYdOz4G1HlowYN/2b4IzvYLSWJRMCkqwdpRE1PHDMA1Q6K7n4rYs5ufhkrpt8XfIcCNpkNFLGUMbYujVdZBRamXX6GJ65/d+0rl7Exb/9PQCe7hCZOUNi2uNz0VY7EM20y895XbOLX7y4mcKeD9AwhR8/t4GFFZkYdaNPpRZPnsaNz71BV8MOtrz1GsaXX6PPNJuuzBlMPSYPXV4eiUQEl2sZBkMu4XA7a15Ko6fZhxyTUGQjkh62r+jEMdXJxf9ciS3YyFxlPY/JJ7HhtuOxGbToRJGzMlM4KzOFUELm9zs7+KDPx7U9TVglkQqzkacnjcGmGdnPiHsoud36thImG81MmfIg20SZ9cox/EoJoRxhxtTURFW/kRUDjuiXW7eTsByBXhRY7Q2wyZf0dUrTapjtMHNraQ5HOCxUmA2qj8rXBFWwHKa4w25KHaVU91fzcvNizp96HVizwdeRdMAdQFEUOu9ZQ6fYxwpdHZXrCliZfwynlpfzl+43qNA4yEwvpYu1HBEby9QFs1m7+FW2yS3kmSqpLvuUvI31WAxaoqIDfUkGP5iQi2FJJ6kfd/Anz3Po+uJ0LpAZf5Idn7EfvzuTjFWdyGg4qaqR55peo7vrRs4qSmDv3IrdbMfjCQ9OD0DSzJ4+Yzx9H3zCxjPOJGRMY+aSlxCNQ34emYkEvzaWwtEXQsEcCoJJq4RTq2F+iZNX6+o5w5/0sQnEhqfuTr5IlRHhzIFALSbTGERRi8UylPArEImiKMkR+8mn/AjvxnZedv8ZV+oMfGPmMpp2uN+lhXgC67JuvNOcLLIYYdoC/mGK4Fn0OJZXroBXWsg49vd8N/cUHmnrpTmcjPRYEoXTxThm8/9hqrkBb95svrejhfemlTGzcyc/T8SpnGCi8itK8LnwO9fw2I3X8sQNVyOIIlq9gWgo6W/ymQAumT6L46++ngd6/TzW1ssp6XZuLcnh6FU1/Lutlxl2M2fvYurfH5xOJy6XC0WvoCu2E6l2EyyJ4urt4Y033iC+MEjJiVlE3ulCCSeIPd+NeEYK7N2tJlmy4sdbYdlf4Kgbk1mid8fdBQDIqaW4j70fc8KD/o3v09veSMGsqsHNsi3ZrL90qBr0m2e9OXzEbnQk/+2CVifBQGLBommziSScrHo9GfU2/qjhDuIGnRFvIumMGonL7OwNsOCPHwFgJsS7hj/zoPVH/KFnFn94p4brjiklzbL76JjMMaX0//WfRGd8C0ErkrvuzzjvfR2Ant732Lr1hsFt69f9M3natJMB0Js0GCxarn16HZ3eMMfJ65jnW0bE5+Efr1i46cJ5w45llERuK8nh0pwIr3W7+f3OTlZ7A/ytuXuEA/ZaT4DaGRaeKtZhjMicXy3xqeUWHAkXTSGJQNZJlHvj1Ng1BGV5UKwAPO4rB18yp4tFErm/ooCpNhOlJr1qPfmaogqWQwBFUfjoiX+x7q1XmX/ZlUw75cwR2wS9USSNwDb/Fi57O+ksOLnLyAU9Np6rrMMdduMwOJKC5XM8lPk85/Qex6xgFk1SPY9sf4ojix7hpLIKjIZcJMFAYqyeuVOPZV31O6x45XnOzTmJ0oy56FZuRtKUc+pPK9D4biN7XCFVbS7+Y9RzRVEqTz0yjTHTFtKwYA6J1jQy1rxCYP0bPBKdxd8vm87di2px95dxir0Ve2cbTmcffQN5oywWy4i+dl97FR35x9FQfBoF9z9Azs9/llxxuwdkedBfBOA0C9wUyOKPjZ08v9PD5f6hB7Y3GON7t9yG3Womd8ZZXH9MKZLBTDzoYSCoAUWREUXDqIXeJI2WGTfcwur7fseWbR/R0rwZsyOFBd/+Nj1x/+B2sa4uJIcDcSDpFwpEIwmIDznIJgwRLo09zYsTT6a/toupi37G9688CmNBxmCK/ta4zINHnctTynF4B+bpo4rCcR+u5qxlb1Nz/EV8b3kPH55aiu4A6zLtCZPdwdGXXcm7f7uXWWddwMwzz2XZs0+w8b230ZlMnH/bXaTm5CEIAleaLNzb2MXJaXYKjXqa5k1i1vJtrPUEDliwAKSkpNDX14d+Whq+/9aTUzQZQScxb948KvLH0t3Vz79NAU4PGshDJOB27b3Rz7DnwckjI2NGcNxv4P1b2Tn9h/xg51+5WZvP0ZZs2jSlmPrdpKU6Rt1tX16QggCJmEw8miDQU0zt6i6gkeJJacw5c3jW1rauZqaVzmDxxmqmjRnDtc8OiaMABv4dP4GHeyYA8O9PG3j+02rmTxzDaZOyObFy9Kis9Bt/wpImiR1SJ6d/2ocmJXmt7LapADzC9ziaD5h37mv0vV3B3LuvRtIO/d7enJPNY8+/jfvlJlaTRyouCjpWA8MFiz+eYOKyLYTkod9AsVHHjwqHhOIPq5t4vjN5/XQFWk7xC7xpEXmqyoapfzyt+lIEexFBXQ41o9zu300z8EhvMsjgigHrnmpF+fqjCpaDhCxHWfbJ93ktamLimiZ61idH81uXfjBCsIR80WSES0Rm8Y6PBpffqT0Pz/tP8FwlbOvfxhE5R4w4TiQYIHtmMZfv+CX3PKtnoyUIFRIT06swaIYcORMzNBhfGseEiJnrz32IPzpzuGLZZtIRSK+oIr8om/66ZuL9IcaVpPOjkB+bQeRXN/8WyWIh4IkQEnIo3ngniGAhxPeeXEO2qYzvORuI+PpYcEIpwdDTrFp5IZGIlkXvv4c2FqGwYvygab983Vo8885hzM7XiBr1eHa0YC8dCD8dxeT/7dw0jJ4YrO0fXBYLfYoxMZ4pFDFDfoVzFk3g/Bl5ZBdNpnPNf9CYnCQSPiTJgMEwuk+LKED+pKm0lI5l7dako+Ksk85Hq9OjDNQSUqJRlGiUWFs7bZ9FpmhFIsdks+noiaRoNcmIn9uTAmRDxwqeTEvhlbZOwv2N3DLxJPpicZ7pGOq7Vxiy3Zya7uCj3i5eO+Fb3Fqn4aYpKfziv6/wx4vOG/6ClGVY8SAsuw8uezUZmXIAZE+YxPf/+TQmW7IP8y+7ivmXXTViu1SthkKDjk2+EOcMuB3NdJh5oaufX4zJxjKK2X9fcTqddDUnfXz6nqzGNDmDuZWzaPnTGo4n6RT+DAHmCBqemr+PjrP7w5wfwKp/kbvzAyQ5Tk84iIzInGnjqalvobGtB5PRQHqqjfRUO4FAiOqGVmwmA2NLdh8m7e0LsW0gFP+df26heWs/lvE2TjitlMxCOwiwo/6PNDU9hNM5D6v9JtJSU5A9rTiseh69YgavbWgnkpB5bnUzv24bKmh4rvQxN+v+y4zND/Dm5g4a7z5l1D44r7yS1iu/i1arRfPLXw0uNxhyEAQd9Zo5SLEEZeK/OOqu/xsmVgA6arbifuUh8qumklM1jVVPP0JO7khxFJaVYWJlnNnAZJsJTzyOaaDa9EZPcHB9uVdmlVYGJGQBfGnXISTcZLkUjnLFCBokFtoVzvzwSmL6GCkXPYClbAF37uEyqnw9UQXLQaLj0X8ivbSe8zpEgueH6KGYCfMWsvC73x+xbTgQw5FpQhAEri6/hrGpZdy26v94rKSVq//6J4q6/sz/Lfs/Fp+3eNh+mzasp662jnnjjuMV01v84cROXBaR43OOpa2+gZLyofn1/vUfY4iUscY1kT/f9yiZfds46cw/csfxFzDjhNOQNBocY/LoXVVHVsY4bBmZ9NSsIvfo+QDEIgnS8iz0X9lE9eYleJdoOKHcyfTwBmp7GslbkMdyTTvl+nlEIsnbrqe3j4TeiLurA2du8mEvmkxMfOA2QMA8exa+5i56Vm0lbcZ4BEFgScsS7lp1FzadjbKUMirTKsleV8iudiU5Wg1KCFFayPLg9Tx1/SSy7cmpJXv+bCLtjQSMbeTmju7L0Nn1Op1ND5OediwX33EP2z/9hNWvvUTl/OMQBAlZSU43JXw+JIeDeHc35l2cCifZ9WTod7HaFB0FjR9zjs/P9wd8V6L2ZN2hP1cU8P38DI5eVTOiH87Fr3H2xlX885IbuWkKlDZu57ncYqb+4294lyxjrH0GBd+ZQ9nYfF5fdicmRWFhy6oDFizAoFjZG1VWExt9Qy+di7OdvNDpoj0SY+wXECwAGZMK6G4JEfm0h2izj4Re5I8kR9MVWomaWILlSpzWYJRC4+hJAA8YUSJaeRHPPrmIBRETzbhxVWpIByoGBEk0HKGr38PqTXUIAkyfWMaOpnZWb6ojxWamtCgpXsMJmW3+EBUWI6vf2Dl4iLYaF2PmZJBRlYoiJR1lF3+QtLBspZIxfSux+a4nN/tFTOakOdCglTh/RvL4l85OzoPFEzJXPbee553nILXqYaAEUTiWwKAdugaBNV24/pv0h7n2rr+CQ6J1l9JjgiBgs07gWu//kUkHkyf9G51uaLr2Mxo2rEFvNHHS8dew6r0XURSZqSefPmK7NJ2G+qMmsj0YZps/zJ317TzT0c8zHf3MDYjIzQGCeTpEi4gsCGx2iCAIWOIeFN/bSPF2dKHN3L7tDqYJGpb54/TGFV7kT8kD/EnhzB+7yC0/cGueyuGJKlgOAh2/fQjv03/FLoiIioz1cZgxzcLk8y9Bqx8evqrEZFAgEYwRa/JhyrUw176AX83+Fbcvv51V0RqaAu1MyZiyy04K0ViMl155FYDN27ZxyZGX0JbXxrnl51KRWkFfdxfdba1k5CY9773x2azsn0jY/SBS6SRmR4NoE3HacqYzc2A07/K4SeSY6FpeQ+YR43BWVtG5agVZM2cjigLhQAytycCRJ53Bm9PreXHJPTwb2kFXfhcM5I+r6qtiZkoqmZmZpDns5GZlJi0Eu2CePXvwb2tBJvpUO10fbyRWlsKvl/8aBYVjm+34P1nBHyrfIi7Fueyi7/DDmdfj6wux+YMY3c1melujCFGR1O4YlCXbM2aOQZB0xFrduMzr0WxMx1CegjYr6Yzb07OIrVtvIIHIk/7xXNh1MrNn/ZeKI5Nm71b3NpzGZJZeKTWVpnVLaNX4mC7l0rlgMr54gjUt21neVMOkrDHs7AtRetHLJB45hYlCBKlrAwDCLtNQY80G2udPoica5/R1dTQN+LX8o/IofvzpIsZvX8+28inMXvcBP3+yiTRvmJ0TJ1Nun8E7T/yLMud/2eRM4WOjkYXFRx/QPbm/VFmN3NfkJSYrPPFpI5829SPkSJy6rpYLslL5bdleonD2gCAKZJ5eQbsrglzt5Z6on7eIccURRdw0NwuvP8gJj27j2dUt3HxixZf2nXqaduLu6mDjcg99kYFkaLo4aanDHbV1Bj35ORnk5wxlay4tTIqUzl4Xyz75mBU5JdzT1I3gi5GVYmDFpROYc1YpRquWbVu7GTc+nZ7uACazSGdnJ+lpp9HT+zpP6m9nXOxDLo/+jZredZQ7Z+22v/9o6+VdJYLYGOPxsedgqGtDrxGHTY34Pm7D82YDdWIHCUFG+x8zGddNAoYXS50w4X48y4+mrPQWnM7R76Gd61ZTUDmJeGuAcqaygXd58LsX8ZP/vJaMbNLsYpGpdrH8b5sAuA6444JkYrxPzDK2MXqO7k3wreYYJX6Z/u4Iq/J72GLZRJ8uac2sqrOTHdMT0siE5W5geHJJzW6cjFW+3qiC5X9E3OWi7si5gy9nT+6RbCy/mAtOTBBc+RG8+BKsWQcnnji4TyIQo+sv65E9ET5zNdPmWki5aiLHaU/GPjPBqp4NLDAdz9VVVyc3sOWCp5WdXcnR77HHGvjkkx6aljUxo3Ia5bOS0UTOjEx2bN0yKFiqVyWPIOkn0WPL5I4LUon1alhe08H8UiexhIwkSehtFhK+KIqgYEhNpS0qEqttIn9sIb7+MNbUpOC6d/1drA2uZXr+ZE5NOZXNnZvZEtzCzafczMSxExEEgXg0ir+/D0dWNkpcRonLiIaRt6TOYkA/eRynvjKXiBLmgWMeIOfxxUQ+3ITjqFv4W//dtPpb0Rkk0vKsLLjsBF64azUMpC5Pz7cOa8+QlkfU24H/ZQ/ydj+et3diO7YA6/x8BCH5IOwmk/8KF1MeqiGydDJHzFmC0ZiHO9RNniNpmZIVmbu3PsCG4HYCG27hyZOeZHLGZBYUjycci3PUHxbT44tzfJmTh7//LnXbexlrbYHMSujcOaxPoiCQqdeyfPY4cj7aOLj8z1f/GoBMjcjNN93IpsvvZu30bzHb/AYNzmrO+sHt0LuQC978IZeVnjMs58dXyUKnjTsbOni/z4MnFMPlCfPCyVX8bHsrL3S6vpBg+QxNlQN9joPTtTKvv7ONHd1+LKlOYtEIZ0/J5YU1LVw1LRUhHiMRi6HR6XFk7XtW3V1Z9vxTrHjx2RHLjygKI1z41Ijltas7ee9fG7GnxTnvluMxDOQ+yUyxobXGuaE16Zuk3dRPj0PH0nE+jkuz09zsITfXhiiKZGZZ6evrIysri3D4DHp6X2dK5GWO5gN04hhivTpMihvyRuYzURSF39S3ownEEDtDXDu9AO08PT9YUIruM+Gw9jG029Zilqy4LBXsTHRS0ZlLtMUHWcOTFhqNuSyYX42wm7IVLVs30dvSlKzVVJVCsCCCdoueeVnn0/bLZSCANsuMMCAkIo2ewX0F4FfP9RPUC2Rq4RiNBgHQT0zDMNFCEBn/c2FWTH1/cJ+7IwKCTotd8zAv6c/krM4GAvZkCRJRI5BRNPw3rfLNQBUsXyKxmJuenkVkZp6GJA332O/63V2DYsVx0YUY55/Hxufa6fjZDwe3idTWDQoWOZqg7+U6ZE8E0aJFm2Mh1upD0IjoDBr8O3dwbMFCjh13/vBOaA2QiNJQV4ter6en9xWKxxio3jafgBwi0R9Gk5Z8WMXjMeq3bUUQBU69MY+2HhOXvBpCERLQezEAG7pCPL+6iRlj0gaL7znLi+jcUE3KuFIM6Wn0tLZgEyLYy5JRNktalrCqYxW/ip/J5N8tRvf2bTxe/zjXT72eqvKhSAuNTpcUK7JC39PVhKv7ybhuMto8ywgnxn5XmN8d8QduXPZDrv/geuypCqYLoaPvN0iyBtuH4+mfHMCZm3TkzS5zEPLFOPF7laQXjHy4WYun4vngVQTS6RW8vL70Jc5cPxfHSRPJr/g7pq7nudT1KBUki+hFo73odOnopOTI2xPxcO2ia5n1VjWndSj8/DsanMakGb26w8sjn+xkXnAxGZKLB+vOZEVDPzZFIeCcSE1NG99ddQ6zco/k4ROGwm0DkThGrYRZSqYUH3b/xGUeeOgZTg52kN2xjLyjsoldaKIn8A80Y86l9IdbwLJvyd2+DMZbjEyzmXiirY8nFpRyzbwxmHQajkuz8XBLD0+19/GtnJHTCvtKyBVAn2NDHGdh++Kkea6m0zu4/oIZeTy+vIlPmoOcPq2I/vYAkiZCyO/DaNm3l5mvrxerM42g18PKl54fsX7+FAcTb3whmUUa+G/tf2n3t/ODKT/gg8drSESr6d6xiEduNPL9e6oQrWl4aleSWnkUL7U281T3ev6W6iSRbaI1EgNAr5Po7Q3S2uZFp5dwpgrIsozRWAAIXOR9mqyff+ZlehWteXmMXfT+iL4JgsB/J5dwbqwO8i3cOq142PqenVtIf/1HfGwygh1O8ufTGPs1gl5Cl2+FWHxEm6K4e2duc4eJC4pvJt4Xo+8fWxEFkbMLb0juZ9FiO7aAaKs/mYxZUdCX2PmWIOBd1ERfPJlRJj3LxLvCWoTeCUgpetKPVaB3DWtf6EaPkUx/Mc0pW3m2RUKSbsCbiPCq+0qOBwL2ZKZfm9PAyddWqVFA31BUwfIlEY/7WbP2fILBejo6X8ThmEFO9nkYjcl555w778D7ejKM0P3Ms/DMs8wVJMxHHIFosWCaNZOUiy8ebM+zpovolj4M5Sm45uoJ+AKkN8roCq30btyAo7QUzCOjbADiiQQr1yRzVrS1VeD1Js2pxx13HEpsyBlOjoYpqpyIJGm4b1Et9y3aCAJMFeo52VTLirzvsGh7N6dYZYKrNgMypknlaI16MivHsuzdJVTMmU7IbISQD1f1Nlw5Bn7wwQ8Y7xzP7L6JBDwv8fMlt5JlzuLiiotH6y5tt3yCjMw7tg+pWP0blPpeqib9nZSUIXO4IsIRWUey8uKVvLLjFTa81YStMw+x9hW0sQpM2mIMlqHR4dxzy5h7bnIeSJEV+p+tIbSpF8N4J85LKhAkCaWiG40rn6Crly7RQ6IvTN9T1TguLWTslH9S6dtKTZ0bSZ+J3T6Zxr515DnGE1y3jtNrfkh/zEOxQ6AuX+K3R/6WfGs+gUick+7/GIvWx88NnRwRW8lj0TOIdLsom1LIC5te4/fbbuOhBxP888RPeK/8PY4vOp76Hj8/+/M/mZ2r44mLLuOcjfXDztHF2amkXfM9mnPsZJX0s8X4GL5aCyaCdHa+wryjN/7PH+KX5ji5oaaFjliMwoGCcz8rziIQl7lpewvjLYY9ZzvdA43NvbzdFebx5asJRuNcPKuA7x+dHGFbHKnYGxuYnG/nxY3dnD6tCAWFUCCKwzKy3MFoxCJh+ttaMdntrHzpucEQ9s/IHVdJwcXXsG7R+7TX1nDC93/E0/+9nek7ZLb8fj5X3380f/teK5J+EgCudZ/gnHcmks6Ir24V+RVHcG48wf3jkpbLioFMxZlZFjKzkr9bd1+QUCxBT08P6enFLJi7je1Vk4hqzbTmzmdM45skWltHr9cFzE2xsvTI8XRHYyPWvfDGYq4F1hgMfGg2clywls4xHkpOPApBK40qWPZEZH0yokeza0RdhYGMhRWjDjA+w35cIZ+5IitxmRN+lfRFSrgidN5XS1yxs1TfSbZJw6wtm7BMK8Puv4m4IrM6MDw1QWahlYXfHk9K1oHdUyqHP6pg+ZLo6HyZUKiJ1NSj8Ho343avoqvrdWbNfBtJMiDodFRUbyO6YweKLNP/5JPE29vJ/t2daLNGZniN+ZLTGcbJGaypX8eWDZu5MDqbUGs36XMno9lDqvZ+l3vw796eIgBsViumoAZN9tAD3WwwIknJW+CSWYXct6gOu1HLvUU1FOx8nj/WncqMNC0F0ZW40+aQlTWB0KYdMLuKSEwhq6SctvYuplSVA9kEWls545Vk5tfTxpxGbbqNV+5eQI3rY34y4R7cfoUsx8j+Gien493QScnsJ4kByLB1649JcX4bXzROIrQIZDch341MHH8y5425AFddsmZLInocojbphBj2xzDbR+aiiLb4CG3qRVdgJbytD9+HLZjn5GBxlNJ7SS+V9TNIfTP5EolnQO7YUgCs1gnMmPoEPf4mtnctZdviDtqXupm77GbyzgnSXyxiuOIivjvuEkLxEPd/Zxpzf/onfjq3mn7TZl5nEk8unc41fW2sve8fdEy4gdgxySRZspD8N8Ezhsf/70HWxfI5RVrOid2r+bj+NLbNreSJtl7Gmg0sdNroDrSRZ8kkdOWZLF+RrFT8b8MfMIc3cGuWf0R+mf0lGo2iHeWluCdOz0jh1h1tPNXexy9LcggnZF7vdg+mQN8ZjByQYHnoo3p+/07SCfnbRxbxncoc8vPNcH8VnP8kmtxpxMIhMqwGevzJ8+nMsdDf7hnhAzYaQa+HHatXUHHE0bz4u9to3baFwqopNG0aCh1uq97CEz/9weDn+MR0SjoUzv5U4b2WD6lKr+KsS6t49ZkcdAYJ57wzAbCWTMGzPVkCoKykjNu9m8jPzWeOY+TgwuE00VvXR2lZ0jK247TTAPBZC2ksOpnC5veR5Cixjg50BQWjfpexZgNjP/cs+Oijj+juc7HScgIn+1dxvq+bTtJZ3dvAgvwDm0rJ/ukMAhu6aHxzBWbFgcYvQk2Y7poN2E4oRJNqHJzWVWLJ/9n1c0JBjiYQjBqUUByDuByN0IMg+Dk34me1r51ZxRO4qaMTWfNX+mLXctSEVHLOLQObnmg4jiXlqylPoXL4oAqWLwFZjlJbezsAUyY/BkB//zLWb7iM3t7FZGYmwwwFQUBflhz159xxx27bC/mjRD9sAcD13HYKBIEskpEfiYYY4W39WGaPrET8GfZIN9efOQ/b2Bl4guFkSnirdbA672ikW/Xce/4kfvL8Rlzjv82NdRNBEnn021PprQ8RtVt59Z5P6OzWUPDpGlzuGFNOzsGZ50AOxoh7ougcQ45xv1+dzOCpE3XcNP0mvjX+OJp7A6xv6Cc7zUSqXkNbXwhvNI4yM4PUbBMZS3+Na/JDxMyd9Mdi/KrDxE3CH3Hos4iEG1F8K4CT0eoljvvueEK+GFlZFQR0MjnZmXh7Q8P8aCBpXel5aCNR4qxI38H05jS8i5rxLmrGeIKeank18RItzVotulgPf+9u45wNhVwwY+gFkW4pJN1SyIefvAPAzjF5/OiVBvJOOZWcy5PhoZMfreSp5QmWP3Uvx19xOg+0ZLCNSv56usymv3UjSE7at/4ZZ+BmPv7NMp6Y8G8ur8/H98xOdhq6mSEnuDVxGW/LM7m6J0yqVsMNRUkhW9Nfw3mvncuC/AXcOfsGJlb+jc1brkMX3kYxO3GmnrPb69rSHyTXYRzM0Lo7XC4XGRkZe9zm85gkkfOzUvlPRz86UeDxtj56Y3GOddq4vTSXo1NGtwDujbOqsvj9OzVccUQRt502gXhvCBQZIj6I+onEZe5f2swHnXp+ftKQ060tPYOepp048/KRNLsXX0aLldzy8TRtWk/L1k2c/pNbMKekDBMsn2ecqYzfTRTYnidxd8GxAOTNq+K64SlIUBJxEokE/eF+Ug2pnGDVUpzu2Ot3VhSFWFMzAA53HUX1TyEoMdJvuAFt/r5XlYakYAHYmnoi55/3e3q3Pc5zH8jYU3df9HNfME/OZMLkMwBIeCN03L0KZPC+u0vpDSHpfCtoRdCIyb8HPgsaEV2OGUEjIooLSOitCJJItlbkjIHt4hoB0aYjf1I6on7o9aQzqq8qFVWwfCl83lGtNhDGEEsmF+vrXzooWPYFvyuMnFDIuX0O/k/a8C5qxqYMFxrazD2PWvVmM/ox8+ld/z5pU4/f52OfUpXNvW9t5qwX+4Bi7jm3Eqszjd7tMRRXJxohDmgIewL4uhMsfayeitlZmGZrSatIJdYdZONlG6nrr+ON9W+TmV3EGWULseqSo7qCNDMFaWZaeoPU+4PkpBopNiTPXWeqEcl+LA5ZS4/7Fkon/ZeajS4cJX+All8jCFrcofN45d51SZ+bH0xC0ogoisLODTVEHU7S8q24u4LDvpMcSpq+g0KELVu3UCrMQCxYhjaSgTllBhVZ8yhNm8Mpsbe4U/Mkvxe38ob3RP7+m2u4Rn4GbnUN5n+Z7XgL7csfsHpOFcFwCqnLVwwe5z9nPM97nt9y1FlX0txwNQJncI7wCrMn3EliXhdbl4rEQ58S8ESx6+1cP+MGOj0beZxmxEgmF+t+yd8S9zFBMjJxh3/Yd9jev52LlsjYgot5OX0cl1Vdy5FHfIrw6XwUokSio0d1NPT4OeZPS7jiiCJuP333oc49PT04HI4DmlK6NCeNf7X28mBzDxdmp/LdvDRKTV9sJCz2ujl7Si4fbu/mNmU8aAQUQYdwUy3ozPx9US1vdOq58qgxXDpnKNWtRqslvbAYd1cHWr0BS0rqqO0LoojOaGTLR4sw2eyUzJiFMJD/Xm8yc9k9D/DP674DgDUtneO/90OKqqbw3uRP2NK3hQlpQ+cy7gojWXTJFzTgrVuJKzMTbdSPVWslJ7eA1uYG8gr37Az92bl/fuFJPHv2KbhEJwXh7/D4UdNI28/r8stf/pKnn36axsZGmprfoTPwJCUT72fNmjWsXLmSWbN2H3m0r0g2Pbl3zCXhjSBopSFhIqm+JSpfHftlR37ooYeoqqrCZrNhs9mYM2cOb7/99uB6v9/PD37wA/Ly8jAajYwbN46HHnpor+263W6uu+46srOzMRgMjBs3jrfeemv/v81BQhAECvKTBeISiRDXVzdxT08ySqKj478s/qAEWd7znLEiK/S1+dHqJWxpRkSDBuvCAkyXpiI5k85wkkNP+jVV6Iv3kitDkUEQMBVMItiydXCxHIvh3r4SX3PSkTQRjw9WKQbQayQemq/lV3Mc/GFeCqeWJYWGNrcYzc56Kmcn8x5MOa2ccccqFM+AmhWdPHffBuKROIIoIAoiQluAq2YcR5l1yqBY2ZX8NBPjcmzYDUNCL8thZPzkLEorZgIQaLiRBwz3EdlxDV3bStn51p9peraHtlo3rTUuWmtcg+ceQUBn1BAJxEZYEiSzFl2hDYfexsnRqcTy1/JkuY/2qgfwC1tRFJBEgZW/XMib8iyujt1IlklOihWA9U8OthX+wcXYjz2G0u07mdDaQ8qllw6uG+8czzUn/408eSwGfTEbmI5HMfLfZadhnv4MF956JjrrecP69u77bgqDZp5VMvhz/Cz+INhB04OQOnReYokYv1r2KzQJsAdgY+9WlIRC7+212FvmJr+jNLolI5ZIXtvHPm2kuS846ja9vb3Y7Xb0+pFTaftCudnAW9PKWH/EeO4am/eFxYosyyiywrnT82jqC7KmyYVk1xN3hUFnJhJP8NSKZi6Ymc/PT6pA/7mcL4IgkJKVLIrp7e0mFg4Ti0ZGHEdRFFqrNzPuqAWIooQgivz4P69yzT+eGhQrAOf88rcUVSVTBjgMDubmzh3qaziOIInE3Um/DEWWUWQZjVZHljmLzkAneqMZV/foGXn9/gjmXSorP/36+zx07mW4RCeCN0qzaOL66ub9PodarZZ585Kmn61btVRO+Mtgza7Vq1fvd3u7QxAFNA4DklmLqJdUsaLylbNfFpa8vDzuvvtuSkuTc/yPP/44Z5xxBuvXr2fChAn8+Mc/5sMPP+Spp56iqKiI9957j2uvvZacnBzOOOOMUduMRqMcd9xxZGRk8N///pe8vDxaWlqwWg+vsDWLZRwA4XAbU2xmzshwMKNiA0uWTgYY5mMQ9HoI+31IOj02ZxqCIFBb3UDhmDz0A4mwEpEoruqtpIyrwFfZSaTOhfNb49Gk7sMLYeBYprRMfI09uKqXAwIoCvax04n7XfRt/hhJa0UQBJSBsFDvjjU4ox6+e/rC5AO/bhsEvWR4I8iFUwmljIVXV/Luv7ZRXNZA9apXSSu5kaAbFE8UTboRj6ufUKwTm/1kFNc69q3YyxAmUzEWy3i83vWUph5FYcUzPPN80uKQMVdHgbmbNe866G31UVg5FIViMGvx9oawpIx88ZqnZxJ9sY4Uez/vVaziSeEOZigrCMXaEYTKZNtWA0/deTOfPHozR75zM1v1Oh5IsfP3d38JiSjMvAoFhYyf/ZTA2ecgJ2REy5Cly7ekldZ3tuERgoy/6h5u3nkR65iOAry74wVuPf03nHJtFSnZQ/uYbBq6PQmuijo48fLfkrq2i/R+K7apQ9N9WklLhimDZ+Z1MTFjErdMuIjQtmTRQWvtmRj6xpFSeNSo53JspoVch5E2d4jfvLGNf142bZgVxe12Y7FYvnCq/wN1rB2N3h0dpJVmky6K5KUY+e+aVmYUJS0lSlzmrc0d9PojXHFE0R7bMTtSCPl97FizgtScPIIeN2veeIl4NEYiHiMSDBINhZgw/9jBfURJQpSGC6DHfnzNsOy/flc/Wz9axMwzzkUOxpHseuRwcjDirV2BbexM3IEOGjwNlDpK8e6ooX/7NhY3tbHw3OHPwK5OP8XFKbDlJTx17/KdGT/mXwPrNDt8WBSFLdMOzDepqCh5frZtayUU0iJJySgojUY1qqscvuzX3XvagFPYZ9x555089NBDrFixggkTJrB8+XIuv/xy5s+fD8DVV1/Nww8/zJo1a3YrWB599FH6+/v59NNPB53+Cgv37yV3KCANhLsKgsTdY5PWlUQijNVaiSjqBwWL35VMw56ak8eObVuICyK+bduwWix0tvZSVJZLxOUm0NZM2qTJIAg4TiqGk4pHPe6o7GI1sRZVjlitc2RgUWRaP3wDCwFQEmhNViRTJplzpgy+1Gxl4/G3tRDsjeJccASBdj/Hf3cC9Wt2UrcujfSicRjtCfz9Ik2dnfRtbyUvLw9pYO7585EX+8rEyr+QSISwWpP5TibO287mJW2E+3bwyev/pWT2b+hp9g193YH/bWmjR4iEtvSiINM867cUoedC5QmOGHsjFl8+6731lGckSxoIiRhHtTwMArRrJJaZjNDVA2/dBDOvQkREl5/PmNdfI9rYiGnAtK4kZDxv76RO6mCTppmSxSVMunAxN794IkdvltlaKPGjiIeiquE+BCarhFa7ipjHxwePC1x06ywqcka+/HfNYBwM7sSYnmznU7ERTcBC9uouDCUjs34KgkCmTU+bO8ii6i7e3NzBqVVJMaQoCvF4HIPh0HBkVBSF/oYudGYD0oDV5MzJufz1wx388NgyQopMcFsPj3/axJGlTkozrMP2bfW1klASOI3OQaue0WKl4sh59LU0se3jD+lra6WoajJi6kYUIpRzAduXf8zrv7qDE6ddTc6Pkta9tIIiepsbB9vfVeQ98bPrCXk9TDruZPQOM7F2P+KglURB1OgpshcRSUSIdbWjaPVsf2cRRslKdPY0tLk5g88CRVYQG5fge/E7HFWYx+Mvf4q56ikCsoJs1xEesLR90OflGKdtv86nIAiccsopvPnmm3R3dxMIJB2hzz///L3sqaJy6HLAoQWJRIJnn32WQCDAnDlzAJg7dy6vvfYabW1tKIrChx9+SG1tLSeccMJu23nttdeYM2cO1113HZmZmVRWVvK73/2ORCKx230AIpEIXq932L+DidN5FJJkoWb7rQSDTXR1v83y5cfg99dQWHAlkBQriiwTicWoXr8Wg9GIVteFnIgix+N4XD4iXa2E+7pJrawarJK7O/xRP9v7tw+b1iERA3HvOlSfksWks6/EWXkkzolHYyuZgpCIIRmHW7YsufkYxk6gf2s9zhwLZTMymTU3iEbnQNSfzdxzk6KifrWb7MIiElIvshzD59uKVpM+2qH3islUPChWYKiSrWebk1Nv+BWe7hDaXRLMCbt+/1FIeKLEjEmrhIEI987/PXl5FyKIAqHoLveNRgfXLINp3+aYYIi3uwbWFQ23YGizsjDPnj34IhMkEWGWgwI5nSPiY0m4wugH8vCcvEbmkg9lNJ+7JmverKNxW5CgJ4gyUJXXaNuXKB0BBAUp1YBR1JMRthLc1IOSGHkOart8bGjuZ7n+h5xgqmVt09DUhNvtJiXl0Eht7mruoae2HUuOA0fukNUsxZy0/Bx59wcce+8STv/PGja0uLl0dtHgNrFEjFpXLVmWLIrsRfSGeokmooPrBUFAo9Pj6e6icOJkLFUvImV8gpyxkUjG7ax67RkMogm5I8KSpx6lceO6YWIFwGgdEgvFk6cB8LfvXggC6PKsaAaiV+TQ0L2kl/RE3f3862fXU2ypYk7GadQfexwfvVbG4g9KiMd9KIIAY+ajSRtLViKB3dtO/bxJPJsnkSixEhpotzMyMmx5VxRFobbLhyc0fLsZM2Zwyy238NOf/pTzzjuPiy66iNTU1IHzsscmVVQOSfbbPrh582bmzJlDOBzGYrHw8ssvM3588uXyl7/8hauuuoq8vDw0Gg2iKPKvf/2LuXPn7ra9hoYGPvjgAy655BLeeust6urquO6664jH49x666273e+uu+7i17/+9f52/ytDkkxUTriPmppfsnxFsiib0zmPsWW3YTIVoigK0VCQ1Jw8qtevY9yUaQSDTWgCWdgLQ8SVfPR+PzpnNr72DcQjMTT6Pb/A+sJ9lDpKafG1UGAbiGrpqYWUoi/1u5lz04n5QwQ7+1AS3Riyyyg7NUbD+628+uftAHTt9HFUmpOmphApKeNoaekkbvxypvXWxyOUzM6kfkUXix5P+mJUzN4lFHwvT9/071WRFq8kTz8PrTZ12PTcuIyZ+CIerPoBv6CsSjjtPqTT7iMvHoWe6mRmWiAmj/7iaO9qQX90BhOPKyfa7ENXYEUy6ji77GzenvZfmsvsXKUdbjmxWJOCXGOcQ/7RD9G7tQGDecFez4VWm0Is5kaXbebIrcmsxamXVIzqP3D8n5cioeDQxokGAywoH4oCSiQSSNLBT28uyzKxcJSM8pFFKM+fnkdBqgm9RsSok9BKIlqgTKNBDsfx4MMVdlGeWj64T7G9mHp3PSWOkqSlsXsbcsyEp6eLoklTCEU7qaWcXwu/40nlPBzFXjrqGni9+UGCO32MP2r4NZh+2tnDPp947Y/ZtvQDANpqtpI3bsiCacivxLN9OfbyOQTbmzFkJi2trYHtuKJdVI4J815Yy7HWBJ3tLRgt+SAIGK9bxXvNKyGUFJQpkkieLsaJVis2TQ+npO052qrdE6Yg1US7O4T9c7WVPpvumzBhuNP1XjS+isohyX4LlvLycjZs2IDb7ebFF1/k8ssvZ8mSJYwfP56//OUvrFixgtdee43CwkKWLl3KtddeS3Z2Nscee+yo7cmyTEZGBv/4xz+QJIlp06bR3t7OPffcs0fB8otf/IKf/OQng5+9Xi/5+xn+92WTlraA2bPfo7v7HazWcVgs4wdH4Z6uzsG04WabjaDPCxIIKBgyUmjZ0E9Yo7B07RrmTpuGa8tm0iZP2dPhgOQIMqHsYo1yN0LFvkclfa6x3a5ylBfQvWwTOnsfhnHzGLcgTvbMDOrf3ETLp2EMJi2KEgNBxukUyMmZzsau/t22t69s7vVRlmKi4NJxrMsw07HDzaRj88kdO2QdkOU9Tz2JAyGREp8L61QUAjE/xfrhTsyx9nY0aWkIOh1kTxpc/nkrCUBzawMGvZE0ZzKXhnH8kIXg1DGn8p2qlwAfHzZ/yIKCoZdh7hgNZTMz2bm5Gk32Dl7NjnK6r550W+kev4tGYyMUasQwzkloax8ApomjW7LmlqbxyY5e7qt6hQ8/7eDMYNLy4PF4DhkfMXdbHyl5o4fbWg1ajhs/evbebfWbyMzJZYxjzIh1KYYUXGEXjn8s4GG5j0siEkF3JfaMLJrqvo805j1Mih9QcNUnr30w4eOSO+9lx5oVw9oyWKy8+/f7Cft9RMNhSqbOGFyXWVI2bFvJaEej78fdsA5vX4iErZDL//IEobWbMP/nYt5ZKPBG2MKxBPGGUhi/awbmgqHInfFZhfwr9jb1hmIy9E7suj07RMuygiQKyKoKUfmas9+CRafTDTrdTp8+ndWrV3P//fdz3333ccstt/Dyyy9zyinJF2ZVVRUbNmzgj3/8424FS3Z2Nlqtdthob9y4cXR2dhKNRnfrEKjX6w84suGrRKOxkJNz7ojliiIjisnvWFBSSt2WzZROmIDHvRk6UzGn69FZ4uSbs9jSVEvl2LF463dgK9n9CyzFkEKdq45Sxy7bSF/AgVLe8wPPMbmEntYADkmkPxhHb9CQujCF9IkmyrKjCIKEL+pkU08UjaaPLMuB5eH4jHAsQW93gInjk9aU6ScXjbqdTiMRDEYwmfb3flDQfC4decLvB42GaFsb+uI9+w2FwkEUWSYjffT6NTOyZrAwbyFdwS7STZ8XFQrHf2cCijKelz/awSqO4MOGFzh/8i/2eMzPBLBpagYoCsbK3efWeOrKWfzfK1t4Zl0bAP2BpGCJxWLY7ftWlfmrJhGJod3v6wYGjWGwFMLnSTWk0uBpQO9q5G9F+di8yfvalpbOCWUXsb7mIR7mCkSUZPa+AaqXLRlRi+iTZx4f9rl584bBv7WfExK9jdvRRbsRrZkUzDgSgLambixTx5GyKsCpAYEcHSQwoN1DGnzBYGJTMItzSyZh2kM+mc/ISzFS0+kjy3Zo+COpqHxVfLH0mCTnTyORCLFYjFgshigOb1KSpD2OgI888kh27NgxbJva2lqys7O/cPTCoUIiHkcQh5vfHampuHt7ceRMoicokz82F384hNOeQiIhE4hDn9u9x3ZtOhvlqeVIu7btKExOC+0nctgP+t0nlgPQmc2YnAV4+jsoNxuwEmRsShraDB3WFBvdYRPa1DRmF5YzPTebPPuBjeJlWaa13Utjs5vCfbgH0nLT6e85MGuOrAz3lUq4XGiczhEVpEejua2Bgvw959e4b+F9PHPaM1Smfd75ecD/RRA4c95N/DNnO6dXXL6PvRYQRAHzjKxB69Hu+OHCskGn5Ac+2IHX68VsPrCoHkVRCIXaDmjfr5qE14uyi9+b0+BEUzCb23r7OLu/FQBzSiqxSDIEWUShrzqVz64DwLq3XiWeSHDCff8c1rY0kD03pWTs4LI5P76F9c3NrG9uZkNTM8u21xLT2UmbcgqppdMHt8stzMDljtI1/VpsskJq2R84ZsGO3c7JxKNx1je6yMur2iexAsl7aFy2bdDnR0Xl68p+WVhuueUWTjrpJPLz8/H5fDz77LN89NFHvPPOO9hsNubNm8dPf/pTjEYjhYWFLFmyhCeeeIJ77713sI3LLruM3Nxc7rrrLgC+//3v88ADD/CjH/2I66+/nrq6On73u9/xwx/+cHfdOOzw9nThyBqemTY9J5eP1r5LvE1LmTWZrfOzqJqJYypo62+nyd/JfsQGDTQ8Fra/nfz/c4TjYcLxMA6DY8S6QFsz5oLyEcs/T0paLq07N2CxZ9If6cdGDqGQl7VdceKCwNH5u8/AuydaOn1EwnFESURRZHKybUh9IayGz/lZxMKg0Q+bvtLZ7STa64F9r9QbaKshYNWRbhme3VWbnU14WzXSKA6pZq15MINpY9sOstJykyHpmzqTjreCQCyWoGxCBhrNvo8FRFHDaeUX7PP2+0O6Vc/in8zj5L98TGWOnUgkgs22fxEnnxEKNaHVphKOdGLQjywncbBQFIV4dzcIAvqSpIC06+3sPP1ejvz4WV7qz4dtr7Hh3Tep+XQpkUAlY6bNpHT8RI4+cRxZJWX4+nv5x/evYNvKT0nZvm1Y+ykZmZzzy99iSUkllpBZ3tBAVX4ell0irNa1d1KYM/o5ESWRtBPvgFPvIqXNx4fLWphZOXIab3ubB0VRyMmx4t7PWj/7i+p0q3I4sl+Cpauri0svvZSOjg7sdjtVVVW88847HHfccQA8++yz/OIXv+CSSy6hv7+fwsJC7rzzTq655prBNpqbm4dZYfLz83nvvff48Y9/TFVVFbm5ufzoRz/i5ptv/pK+4iGAIIyaRdTpSGVC8TTW1G7C2+AlPz35stdqNBRlFCCG9z9pFADZk6F1NeTNGLa4PdAOMKpgkSNhJO2+OWHmFFbS2rCOuNVEXFIoSrXRRYLSPdQ32hOdPQEsJi35WcMtMm3+CFmZu2QrVRTo3Q4a4zBBJkniXv1YPk/c14s3RaTCUjRsuaDRYJw4MhQcYGL6RJa2LqXSPB6daMBuT6FxRz8ZeXYcqcmQ6kRcprXRTWHp6FlWd/ky+9XfL0KGzcDqXx5LX1/fYJTIgaGg1dqIBLrgSxMsB/bm3PXsyYEgksNBwuMZtk2GrRDvsd+n/4c3AArr332D0knTmHbGuURCQRxZ2ZjsDgB8ioA9rwAxEiYSCDBm6gxiokTMaOaMSy4fzJqrlUQqsvIwaPfdmpE/JouW+k6KxuaSn2slN9uMq8aF4otj3aW2jwCU5zlY2u/jqAMsa7CvqO4uKocj+yVYHnnkkT2uz8rK4t///vcet/mszsWuzJkzhxUrVozc+GuAoijIewjRFkWRmRWTR10niBoS0RCSbt8q0A5iy4buagh7wTA0mtYIGuKjZNxVZBlBs+9+BKKoIb9kGh07O4mKEdIteryCTMZeopp2RzASJyt95DSFwPAcGER8YM0ejKYY3E4QDuAJLB7QKNPlF6htb8aqzSbs7UVv1A6KFWCwXMChRiQSQavVjpiy3R9EyYjPV43BMDKi58DZ/3OlKMqwHD+i2USkrQ3hc1lvzVozb+18C63eQCQQ4NK778cWDOM3GsgqK8fX243RYkVWFLwGE1f+6cERx/qwz4slZeg3FIkniMRkGvuClGbsm6iQJGnYPSGKIs7xToK9ITo3dJFemYakkUjVa+kNRlBQ/ueVt1VUDgfUtIdfMZ6uThyZw0ejG+tWIooidvOeR7t5OTPZ2fQhY4oX7v+BSxZA9esw/vTBRTE5NixHxWf4mxowF+zf5JMgCMwak83HtT1oJYG9pM3ZM/v6gtdbwdfJaKNyZX9H6sKBje3TjWmUZxRisxuQpNFf/rFIgsa6PkwWHRnZQyPoaJsPQSehTd+zr9BXgdfr3e/ihp/HoM86pKaCPkMQBHR5uQjG4cK+uq+aublzabrMwuw5Jw0ul70eoqHg4LxIXTDC2N2UFCg1GVjnCTDVnhTUvf4o6VY9Lf2jlzrYHaIkEQ5GMOziYGxKM9LZ00NvXQemNCvONBs72r1gVMWKispofGGnW5XdE4uEQRCGVY6VZRkFhYklMyjI2ktBNFHEZs2jv2//nWgRhGTCs9p3BxeNsY9hbMpI3xYlFkEyDH+JxqIRepp2Ist7ViKZNgM+AfIMuzGRB/qgY1Myod0o9LpDWC37aN0RBDCnQerIUNb9tmooENtLfafd7EZKqmm3YgWgrDKDojInOp2GhuoeAMJ1LhAFom3+3e73VdHX14fD4fifH/erQhCSNat2RTSbET5nPeoN9ZJtzqZ08gy6g92Dy002O0GPG0WW6YrEcGo1iLuxaOQbdURkme5IUuhn2Qxs7/Sh3cP1H43C0myaGtppbeikq613cLkCZI7LQ0nIdK+poT8Ww/E/Sp9/KFoCVVT2hGph+Qpxd3aQXjjcclHTvJGKgqp9biMtrZzGxiWYzJkYDPsZimpKSfqzNK+AgmRmVkkYxU9llBT6/r5e0gqKcHd1kJK1e0fasVlWntneyQljRg8xJeyGrInQWwvpI516+91hxhbtR8ZV026sUvv98FVGPxejsKyxmrgsJ/XSXnJi7IrDaURjlKjb3I0mGKO4LBfRpCVc74YvEJEvyzKhUIh4PE4sFhv1xSMIwsDUiYLRaDxkI+4ko5aYP4J2X0XrAMouU0nhcBd6fQYtjQ1srPmY6VNOQGvTYo2IJHw+0qxprKpZhTHXOJh/Ji0/Wf5jw04Xk4v3fP/NSbHyZrebk9O1SKLAxFz7iCKbuyMUaiYWc2OxjCe/KBsFmURcoaGmBY1WO3jb2rJSCLXvpLG+hQuK9p5/6YuiEwViioJOnXpSOYxQBctXhLuzg5TskXP94WgQw17Chz9PUdE8Gho/xGkvxJ4y0rqwR6yZ0L1tj5tIBiPuLWtxVE4btlwQBOT4nq0QsiyTSCjY9pQ1NREDzUiTe2unj5xRfFf+V+yLn8AnjdWkmWyMTcs+IP8Pi0lH2cQMdtQlk7xp7HoSngiy78Dn0LzeCCZTEJPJitls/kJ+KQcbR7aTvoZO0kv3L7pMGcgZtKLmAwQhRiTsw9caxZmah8vVT1e0myOEIuI9vcT0WiwDOYF6enpwOp0IgoCrI4hZI9Hni+C07lkwzU+18kG/l4XOpFiJyzKagfMeicfR7uYaKIqMxTKecLgFk2Vo8GJzmAkHI3Tuam1RFKbnfrXOtp9hlERCCRndYXzvqHzzUO/Wr4hEIo7mc6Pa7c2bKMmuOKD2xhQtwO1t/TK6NgJzYSna9Fyi/V2Dy0z2FHqadmLPGN1nIZqQWbmzn81tXk4tcA4+vEdgzYb+enAUjFgVicaxfEm5Iw7AdXOvRplPm6rxRyNUZOR+qaJAX2BDtB54WnynM59otB+dTndYixVgoP/7P8o3GIzsqKtBq4SZOfZ4RH+MeUedQn5REY3Uk2PJQZBEUGRqO2opzynHarXidDrZvnEnnu4Q9gwjZfk2Wvv27o9i1kjkG3S80+MhmpC5r6kLfzwpOuv73ZSkOkbdT1FkotFu9PqRGXsNJj1FZclBjaexEUteHkga5Nieawd9GRhFkeB+RtapqBxsDu+n3SGKt7cba+rwKZJwJEg0FsFu3c3UyT4gCAdwufbwVk743UQGQkEVRYJdfDr0JhPphcUjRNdnbGh2M6PAwaR8Bw7LHkSHzgQZ40YkfojHEmj2EEYdT8hIX7G5ek/Ny7JMX9DPiWMnf6V9OBD0eguxWOhgd+NLZP/lZk5aLukaE1JtlNot75KTmo/FZic/u5Tt3QLlqeXIoRAMTIvFY32EI52IoojRbMCRaUIayJWzO/+VzzPWbOQ4p5V/tPZwRU4aH7t81PhDROIJTLv5nRiN+SCIg9Xcd0eovx9LZiYZeTl0tbbv38k4AAyiQGQvma1VVA41VMHyFWAwW4gEh0ZtwXCAT7a8z8SSGXvYa+8ckJOcuxlsI6em/E0N+Du7kf0uvNs3E+9pRJe27+GqOo2IuJ+Oh7vS3hMgew9F3brcYdL3w68hFti/qA0ASdAQjvkGP3tDXTy66lUURaE36MO4H7k29saIa6e+KwDwtPVhydj/MgGBZZuROjzk5OaSmTOFMeOGCqzOHEj6t0baRJ3Jkqy3lQggJ0K4+7xYHcOnIR0WLT2e8D4dVxJFTstwkKbXclK6g7ii0BDevUVEFLXDIqv83d0kIpFh2/g7OzE4kwMZq9FAcMDBNyHLvNW6ka3uL1/AqGHTKocjqmD5CtAZTQTcLgJuFzUN62nsqOWYKad94XbTnGW0tu5nvhpXAzhH1iOK+z3YS8dizC3CVj4Rx8T9FFNf8IUblxV02t3ffmFfBGPK7hPRJRIy1a3b8QS9AGgt++cLI2h0FFsnUte7ms0dH9AfaGN5w7ukmXN4fsvHbO1qodT55YTwhiMxtJ+3JqnvCwAigTBG2/77MYk2M5YjJoIA9tQMYj1BFEWhz+Wlo0+hvq+LLT19rO5rJRBO3qyKksDd58OZ4RjWVm6KkQ7XvlusCo1DQrrSaqLYvZWIp2ef9vW3ttK1YcPwZe3tOAoLBz9/5lD8aW89C7PH0xXy7nPfVFS+zqiC5Ssiq6QMOZFA57AxvnjKl+JrYLZkEY9HkEeJ6tktsgKjHFvaj8KR8fjw3C393gjmvdSwGQ1/MEo0nJx2EvYieJSYjKgbfcqorr2e7e21lGUV097fybaWmn0263+GuXgKgcaNRGJ+0s3F9ASa8AYUzHod+bZMFpRUUpQySqXgYD90bkn+v4+0tngpKPi8FeGLKpbDX/EEXD4k7f7fR4lQBCU6ZNVI9IfROAwk+sO0d/Rw7oKj2NLZgZyYxgRHDpmpJYCAIEjDoos+s3qJori3up97JD2zAF/HDhLhvYesC6KIMSuLYHcyzDri9aIdpbZTd8iHRhDRS1ompuSyvr/lwDs4Cls6uoiGv07TiirfBFTB8hVidaYhfck5FfKzptLavnbfd9jNNNK+zC4lEnFa6tfQ0bhx2PJGV5Cy9P2LZkgkZNo6/TR1+XB7925+j+/mDdLn82DQahifX4FGo2Nc3liK0vOJ+uuJ9Hfsc38kSYtCgukFp5NlK0aJpjEhexYLxkxkdn7Z7ncMuSCrEgL7NqIOB2L4A7EvNH02OofvnFKg30fX9lbioRjO4lFE4efwer10dHQQG3BGlYx6SMgEVm0j5vKjKCBoRZTEQAbc3jrOGFNIpTmXDbV9FDnNmExFmExDUTqRYJCepp14e5PXMRZPsLnRPeLYsYRMNL73AYKzfDbetu30vPMMXRs30rVxI762kYUiFUUhpbAQT0tSgLh27MBZPjzc35Gawroda5mTnszTlG6w4o0G9tqHfaUrGMaqkUjbn4GPisohgCpYvkQURcET8dDoaSQhJ1AUhYTyRVLAjkQyWJFj++iv0VMLzpFh0F5XL5t7obF196ZmOSGz4cMVaLQGFER8rmQEUTQSR9yPwn6f0dDiobTQQVlhCv5QjEhkz+HSmt3kuWjtayE/vXDYMpPBzNgZxxJ1deBrWLfffQMIRJLnVBTFvVvD4hGQ9uzf4naFqKnpobM3wORJh1522INB0OWnq6aVRDROZnke9px9q2sUi8XIysrCvUv1csuREzHPHI8o6pFMGqIdAUSzFjHiAZMTgv1kWQx8e245bdsaSUQHLHsDlqmg101G0Rhi4aR4nlGWhtYoEfQPtyY29ATY2bt7sRCKRtBKWgRBIKVkGvacXDInTSJz0iSQpKR42bCBrg0baP34YzQDBRPNmZl0rV+PzjKyonlaRjplStK6GG1uJt7TQ445gxZ/94htD4T2/n4K0pxq4jiVww41D8sXxBPx0B/uH8y8qZf0FNgK2OHeAUCxffcp7+VIBEGrHZGhc29IgkhCTiCJewmN9bYlU/TvgiLLdLQ1c+ScqWzc3jtil84GD9FQ8uFeNW8OWq1EZ3M1Pk831pRMtnT6mFTg2K/+un1hzCbNYHbYvEwr7GVg3R/qJ55woNklv0tdez1FGSPDowFEvR5ryVQCrdvwN2/Csk/J+YYe2Ca9kXF5e69WTeoY6K2DtCErTCIhU7+jH1ESQElGOJlMOioqRlbkHe3YX3dkWaa3rgOtWU9mRd5+768oCpFIBOPnUu/LkTja/GQyPp1Ji88XwGzQgEaP1+Wju0dLZoaZvAlFbHtrK2GtSPGUpGO5zmAk5PMOWr48wShtnX7KJw63HOo0Iok9hP/29TeTkzYkoKXUTOKtDWjyxmDNysKaNSRWE/H44G/dlpeHLW/350KUNElBIcsk/H7K0ov5pKuOfMsXK6/wGarTrcrhiCpYviD94f5RRcloKfA/T7SxEUGS0JeOdIrdE7nZU2ls+YQxhfN2u43c10OsNYhk6ECTmz24fGfdVkrKJwIjw3oD7ggIUDDh86HXCczWdOqam3EYU/c73LjfHWZMvmOftg2GA0SCYdKcIh9v+4Q55XPY2b0TALvJht1k2+P+5rzxBNtqcW1ajKWoCq1tD6JhHzPdDt9HGFYpGmBbdQ8TKtIPyPL0TaBnexvOkmw0ugN73KSkpODxeEZUmg72uTGlOQY/N7Z0MKGiCn/deqL6VCrHp2N3JC0a6UVZxHUueht6SU23Y3ak4O3txjGQxbmlJ8jCSdl8nj2JFQAlEUfUDFnbpLxyomvegbyRls39mR4WgP/uXEm+O0KVZWS/DhR3OIzxf5T6X0Xly0Z9wn4BEnJin9O7j4ZoMOw5Gcju9tOZsVvzaG9dOXq/utqRfT70805BMkvE6rYT27Gd2PZtREIWNNqB2kafG+T3tfvJKh4ZYmq2ZuDqb6Y/5mLMPlao/QxZlpH3w6Oxq70NRVCIB0KEfA209zdTnlPKuLxyclL37cFtyh1LStVCepa+gKduJfLuXjrKSAfM/WXHjj6K8+0HRazs9nsdQkT8fiS99oDFCiSrHX9erCTicZpXrMdgHz6l4u0Jo8mrQjJn4NnFVyrkd5FRVDQs3UBUY+WDJc0Ayey1iZHnMy/FRLZ9/6qla8fNIbr54/3a5/MIskKWyc44Wy7rI70s7aqhxvPFE0dWd3RTkfXlWGlUVP7XqFL7C9Dia6HANvoUxb6g7CXt/Z5wppZQ6+mhfeMHGHUmQEHUGjFrrQgaPZqi5AhPcGSgdQw9oLQ7+gb/Nhq0eLwR7LaBiCEh+eIOuF1YUoZeENaUDCyOdPzNnv3u544mN2P2cQpJlmUEQSA1JQ2z0UJE6cFmNSDubeprN+Scei2ujYvoWvwvUqeeht75ecGTfEE1dDaRd4AhzPFoAot99+HXu+ULzggZjQ5CITfmvVT8Pth0923BoUv70tuVNBoMdis9m+sQtVoEQIhINLa4sDgMeHvDjKkYOjeiqCXU78Xs0ON3hbGkGOjqDDJnZg6r1ndSUuKg3RWiIG14xI5hD8kNgVGvo2C2I+gM/D97/x0kW5re54HPcem9Ke/d9bZvu5nunp6ZHnhgIJAESQ1JUGKsFqS4ilAI2l1gVxFLKRikYimtyAiR3CCxNEIwhhJIkCAEDIDBmJ723de78t5mpfd57P6RdasqqzKzMqvq3u6B6omo6L6Zx+c553u/1/xeY3sNKdq6ttF++s+/zPSdP8L/6s/yZaoe2FcjzZulHsXdxc8YCbz4TuFnnHFanBksJ+Rg19h2kDs6ENsoL95PRS1RLBtMXPva7md6KU/84RRdr7zUeMV9L9jxQT/3p+Jo72/gjzpRSwbprQ1sDifFbAaXr+ptefBkm7RmINmq51rQDdxy8xe5ZVlML6bo6fAgt1ghs7aySG//EAB2h4Mr577K9OYPibjbz3t4hvfc62TXP0OfW0XChhzeH+4S0A2dfLnASNdgw200o5lab1NOmEPg93cTi019oQ2WdHoNl6cDisc3zJvR3TmG7VwQaec3iAC5ZJlyQWPgmp9COUcqVSYYDNL/8hBT33tI341BkptFPEEHCOByKdy4HOXhZIKyQzxksDSjVM4jNhAXVM69jHrnjxE8fkRn+/2BREVhyFtroNglpcHSR5MuV5AEAc8+Rd5irkL4+MLbZ5zxwjkLCR2TRClByBkiXUgfexuS14twQNJ7dukxd5++TyabaLBWlYfTH3Pt/JdqPpOdHmRPG52PgZWVLL6wg/FbnQxcDoFl4Q4EKWX3vCmiKLKwtUnvgINP0nn+1UbzYwOYXkgx3OvH22KvoFKliGmaKHLtS9mhuMmruQZrHY3scNP/k/8VxgUnxe0ZLLW2CmRydYbLA8fr73QyTuZikSTlCx0S0rQSufwG4fAINk+YcnrzVLevJ0tIESeSIlWl95Mlsj9aRU6U8JZ1xLKOaZro+7yY429fIrtQQkvu6Y9YloWiSNy80oHDqzCXbV0xeWP1Ib2de0aFsbaCPju1+yf6Bih/XD9s2wpdfb2sL588DGSYJk82NhmLhqpaNJbFk3tz2KXPr/HoGWcchzOD5ZhkKhm8ipdPpj9hNXF6TQlLlTw3LnyZ5c1ZUpnDVTwAj6Y/4cLYS6eS6d/V42X4RjUx1Rt0IMkK8eVF3DshobszCR7rRTrHBe4kFuiyK5xzN4/pTy8k6ev1YWsg/FaPleUFBoYOu7z7QzfYSD9s44wOI4oKPu9lxN4w5ZkZjNyeASTLNpK51vVbTo8/vVUaqlpkbe0O/X23ALAHw6jp1KnuQ4uXsHVVB9zKbJryVArfm304x4M4xoNUlrO7IcZniKJI981Ogj0epjenyZsZdG3P6LvZ5Wc2VWrZEJTte/kzxsYqlq4hj52r+bPdeJXCnSn0VPtGt9fnpVQ8nrjbbDzB3dV17q1tcGdlnVcH+5FlH6LoYObxIqPn+7HZj++xOeOMz4Mzg+WYyKLMg+UHvHnpTTZSG2ylt45e6Qh0XUOUqlG6KxOvsplYZnq5drDe2FrE6w3idhzWbwDQtebu9/3z+sdTcWySgLwvYdQX7SDY04fD7cE0TfKCxZ+/3I9ol4jY7Qw47U2jGWXDxGWTcdvbizY6HE7Q946usrCAurqKIEi06o1IajpZvbHujd0RwRxyouWSVOYXwCFyvneU9XQBVWtfmGt6IUUlUUbNVhouY1kW6+vrZDIH83/+dJY1Z7KrbG09ZmjoyzWf28OdFGOnZ9iLikRpKomerXrMPK/31HzvvBSBpSJynYoYW5+M8OATJHuZVK72t3ut28ed7daMC9nhpVzMYKwvgyAiDx6uDJL9Hmx9XWirMSqr2xTvz7R6inWJZ3Ok8s0VdU3TZP7RQ650Rbne283Lg31IoojNFqZc9OB0ObA7Tq9P1hlnvCjOcliOiVk2iXqjOG1OXh57mXsL9+gMHK3a2YzpxQdMDO/ph1wYuUkml2Rq8T7nhq6RK6ZJZLe5PN64789RUueKIqJWdGx2GVMQuDpxOIhtVjQMBB6uZXl1qBpiUgSBfneER+l1oL6xBLC2kWW4s/H3jQgEw8QTW0S7qomxgihi7TSJExAwLfPIfKHkjrHma5BfoyhBFCVI3prC3T2BbHZSKi1zZWCce4uP6Q/3EPHt5YQUVR2nIu3O0k3T5OFkAmlHS6Orw0lkOMjWwzidV+rnImWzWaLRaI3oGdCa1PARZLY0tOIBNVUL/EEv/pDnOajrNsYwVDY27uFwdtDff/j+tHtDZJMxTMNAlA7/PqZpUkqsY+RzSHY3CBaWZeDpOWwEANiH/Zhlg9KTBKLt8HmKsoi/N4ygHv7OtEwSrj6ya4uMHRBW9NsUii0o2wL0RAd5cuf7TESGkQd6Gi6ndPhROvyUZ1ZQF2O4rjVRUj7A/lYCs+vrlFQNwzQJehrnxUytrvHK9Wvc//B9XnrzbZbnNsnnCig2GVmSGD53/JywM874PDkzWI6JT/bVlFrK4skvpWEaKAcS6/zeECubcwDMLz/l2vnXm27jqECDCewfLkzTQhQFDFUj/XQRvaKxtbWBde0mCGCTJVbz2/S4ogx7Iry7NYVgO2yQqJrJ4moGu0NCbJKIapomqqricNRW1vg8flZTSeZnpxgcGgXLQtgZ2PqDl1lM3mMkfLPpuYkIaC0YAi7XCPnCFG7XMCBhGAVuDF/mycokEV+ImYU0asVAlgQyeZVXblQriB5NJzk/FsR+oEQ30Osms5bH33t4ELHb7ZTL5Trhu5OHhIJhH92DtVUopmmSSeVZmd9o6sOxKQrhziB2Z+39Vi5WKObLuLwOHM7WEsITiWmKxRzd3TfrejSe4ekbJ786g2+wNmeokk5STm/i7hxEivRiGBUwLEqxFUqJTSSXHZuzajjnVmcRJBlP9xCiQ8J9s2PXy3LoHLs9lJ4mUDpqK2O8Ni9CR5BR/xiB41R4AXmtwsfxeTxOD1ZXY2NlP47xfsxSfW9cNl7C0E2CXXt5JZZl7Srzzq6v47DZGens5PHSEo8WFymqKi6bDcuCfLHIaxcvIAgCuWKRCwP9nL92g/e++8e4Ose4ef1kFUZnnPFF4MxgOQbPBt3TxDD0hjkplmVSqZSRW6gSOGq4NjUTaSdc09/jYXI2Sb/DoLwRI3R9AiyLzJzIWJ8fCygUisQqedJqCZesYGFh1tGrWFrNMDboP1LWPpFIYFnWIYMFoK9/GNM0WVmZx4jFGRiuvmTtig/DODoZsmyaLYnaiaKC13OeQmEWt3uMQmEWWR5DyJk8nkrgdsmMD1c9T5lchdv3t+judGOZ1iFjBcAecpJ9koA6BovD4aBUKuH1HjDynlMKiyiKBMM+guHmAnvlcoX4VgJdNWruGZvdhtNtIxFLo+20T7Asi56BDuzO2jCCqhbZ3LxPIDBKf//RQomiJCE5XGSXphBEAQSBYnEZt38U/9DF3eVk2QEyuDoGUfNxjFyZ1PIdQMCSTFyRfvJrc3h6q/eH7Gsc3pBDDvRkCTlUzbsydQNtvcA1YYDVLXVPcbmSg9QSBIeO/GkerM2Q1gp8bfAalWCZjfWVuvlXdWlgUJuGxcHWPs/eB8vb24iCSF+kek9eGRkhkS/gtSnYdpL2CxWVTyYn6e7tZygYAMDt8xO5cInZhMm5lSwOnx3Jv88I/dMZlTzjTzFnBkubFItFisUinZ214R+Tk1VsTM3f59zwtbrfTQxd487jH/Ha9XeO3I7VRuVIwGNnZmsBLagQffUyUO0h5LCHuf/ehzhDQSzL4uaVSwD8/uo9FFEmVokDewJzsUQBf9DeUkdqURQRBAFd1+vOxkVRZHBwjEXTQo7uqdQKLaRbXfS0LvBVKMxSrmzido/t5MlA0C3TNVgbIvN77bx0rZPHM0nGBgMNtyfKArpqINdJNA4GD1du2bwhyukYjsDxRbyEE1g9Doed3sHG2jPB8N7va1kWKwub6KpOZ28Ep9vOxuY9BET6+l5tqxO5u7NWt8iudlAqzQNDh5aV7Hac9qoHydlRq6FTkWRyyzN4B5qHV6SQk8p8Gj1bwSoZYIFtyIfkUpBm9lW75WPVppbbU0Dj36SkqZTX13g11I+mqSwvzTNx7lLTY2gVUTr8e+a2Z/FnDEZe/bndzzIzM4THa8/bbbdxY2yc9z/6gLde36seLOo673SEeFhSUdZKXPefLGx9xhmfJ2cGS5uUSiUikTpCWCecrViCiSLXnynabQ5ev/GNI7eRXd3C29Osfw3kCyrT0wlEEbxuGwGfByd7bupsvEzfSASX006k10c6ts3q+iZ9PV38dG/VoHo3la+6q3dmgLmixmiL0vtV5VuzaeigHm57hExhA7/7dGTKXa7RamfffVhNDIBL4831ToIjfpKzGSLnW9NFsfvD5FZmTmSwvCgEQWBgpHrdl2Y3qOj3GR59G0U5XjhlPzabn1y+/YRnuzeEIIlkF6fwDTXuASUqIqJTRo44kVy1HsonsbsMj++bBFgW7IR299/f+/kkOc+r4UFMQ2dlfobxdo2VBh5Am1NG1w4njA/rBei9sPvvwtYm6cV5BFnCN1ybf2NTZEYD/poWAJZlorgUXgo42NjI8Wg9w+Wew2rWZ5zx48BZldAp0czDki/nyZQyrKfWGy7TigfhKErJLM5Q85fRxYtRJibCDA4FUGwSfQ4Ds6Kytb7B0uIyjz/6hMnHT1iaWwQg0BGlmKjORAVBQBAELrodPMhVQzR5VcN1hIjcfsLhMIFA4MjlBEHYlcuPbW1QToARr1/mfRwEQUAUD+RonCARVpQlrDZaEFQP4ti7q64uChjG6XYDP4rBsW68zv5TMVZ2OeZlt7kCOLt6yMw/Jb7VuHLGPuA7ZKysry0zNDjBSmanui8wAFuPITDIiN/J08RhI+pecplspQiaxsb6GiOj59qXFmhwj7l8NnzhA80dc0msRIzM3CrmTlfpzOwMg9/4ScrJo7WQ5uIJBvx+JJ8ds6DRMxjAIYnEn1VG/emtrD/jTylnBkubNNJo8Nq93Fu4x/T6NOlCmjvzd/hk5hM+mf2Eh4sPiafjbCZPVzxrP7lYHE/kaNlK204OhiJLhMIuzGIZ541z5LN51p9MMnDtHFpWwBPYG8xlfwBjXyll1K6Q2KnIiW0V6epqXclTFEXsR6j7moZJpVRNVE2nE+iqxvDIBLJ8+roRmpZCUYLounbiF7gr5KCw3YZuxgm9ct5AmHx6+2QbOSZfFNE6xeGl7LMTT2RYXUxRKmktrVcsFxju6CaWz/FwNQ2SUg0JSTK9Hgfxcm2O2kw2xnRmg9c7JlAGB6Gnc68nVx0sy6I8tXD48zaMWvXB+ziHztH3tXeI3b9H8skjAhPNO4qb+7yGqVyO6E44Ug47EUSBsU4vi8nWxfHOOOOLxFlIqA3y+Txud311yNHuatLdVnqL7ew2N0cOV7RspjdZT6zTEz5cVWAezLhrE9MysMnty/zbB7uoTC+jbea5/MbruD1u+oe6mZucYWF2nu2VVXA6GTzguXFLEt9PZOk5IM51GqyvLTE0PsHGxiqWYdLT/yzvoXY/lmVhWVZbORS1WGhaGpdrmJWZR/SOXjx6lSa4u9xsP03gjrbXLO+4ONw+ssk4/hcsrx7uGiGxOU+0p70u4w05QXsLqMrOn784QnK7wPTDLa69cnTZrt8bZmppmbuJOFG7kyt9gZrvPTaZP1lJIgtVeaCUHuOXh18FYGlhlv7hxudu5AsUH8wiHDivdhtsii4fyoVXq9VyAhiqiivaWggxmc8TctXvGyQLAkadxPkzzviic2awtEG5XK6fv7KPzkBnQz2WrkAXD5YeHDJYdE2tVk2cAJffT3Etib2jNQ0UPZ0n/6PPsPV1I7qdGOtxvG/f2P1+YGQIwzQYHhthZXUd8YCh9mrAQ6msMV9uP//gKEzTpFjMIyLQ2beXpCkAhZUn6IXkzr9ELF1FkMR9g56Azd+Js7t51UY1f6U6gGwuzxLu6j+B4bOHgIBpmC1poEhOB1ohj+Juv9cMsHO8L77Uw2Z3omuNxfLa5/jnYBgaJbXMdmabaDRKNlVmazVDZ1/z0GguJWK4TH7+8gUw7DxZz5Cv6Dxcy/IXbvVxs2OvyuqPZu5RrFRDMqZpYlqHW0jspzy1guf1q5QeTGOZJsLOfVWeiiM6Wn/l2m58Zff/u155rea7epV6WrFA6dFDuHqDpUSCG4N7/bE2M2VyZY3xTi8Bl0I8ryKZFrpuILcR0j3jjM+TM4OlDU7Dk6DpGqZp1gyOkwv3ODd6o8laR6M4HGg7L9VWEJ12jEwe189XXczicm1oQUlOotjc4Bihv6++zsRKosiF7ubls8fBNEzim5uMTdR6PPR8GlGv4L/0VtP1S/FFUve/SyW+hu/il3F1H54Nl0pLOJ0DlErLWKaJy3s6iYjufgdbUw/pvli/4ms/znAf+dUZFPfRJcFfNCTZhq6VkJWTeZN0PYcoHP81tLA1h9fpwueqhj6GJsKk02WW5pMMjjRPgL7Vv1dp82QjiyDAn3upj48Wknz9wt6kwynb+Cvj14Fqg86+nQadjRCkaq6XfWSAyuwyjokhyjMplA4P6nIaI1dE8p6sa7Knu5vc8hLegT2jJHHnNsM/8wtsffIxYmdtcnpJM3AoEiXVQJEE1tIlyhJcsuycpeCe8ePCWQ5Li+i6jlRHobNdzved54dPfliTA2BiHRKMexG4rlaNFcs0a6sXTBNsbjCOzgc4Da/EQWS7zOj4hUOf2wr+I40VAGdkiOC1d+j6+q9QmLt96Pt8fgpVTWBZBoJgI58+KJt/fJKxJfwdIQrby0cue5QB/DRfYq7Y3Aj9vKQ0It2jxDcO52i0SzL5IcHgq8devz86RL5colhJ7n4WCDgw1MYhj/XlNJ19tZ7I9XSRt8Yj+JwK9p1WFdlKkfcXn3I+uhdi0lQVSWrNwJK8ToxsEVOtllPLISeu6+OUHi2cOAfI0z9AYWOvB1bq6RPc/QM4AgFc3d1kV+rff7Ik0Bt0cXMwyLVuH+V2E8XPOONz5MxgaZFcLndY/OsYuO1uvnTuS/zw/d/n4fTHPJ75jJ7OoZMfYBPi8TiVSq0LX48lKD2dI/fubbTVLfDu85QIQtVoEZobaO3G5Fulr2/40GB+nH3lUimcfecoJ2rl6zPZe+TyTyiXN3A4uvEGj55jlnJZKsXm4a9cOo7T7cUV6UeyOcisPGn7mPdjF0WMI0/78xlwTsNQNYwKhtm6V7AedsVBV2gQ06hNku0Z9LGyUL/hYrmk43bX5nt967Wh3XOygJV0gjtrc/T7I0Q9e89G39AIdz/+kHQ6SSMERcYoV583pTNE4ZMFbKN795jr1jlKn062dZ71kOw28murbL3/PrLbjXdwCABv/wB9A0Osbez1N/M5ZAzTQtkXqnSKIqUvSPL0GWe0wpnB0iKmaZ6KhwXArtgZ6hjlysSrXBq/ReSUxJzqzdpyuRwej4dcrrahm62/i8AvvI3cEcLSTeTeffotglD1sHgaa7rEkkXCx5Q1b4Sl62iTd9Fnn6DNPkZfrDaK0xemKD+dxzHW39J2DF3n0Wd3SRbLbBTsPJz6jIq+NzA6HX30dP8yYCIIYktDfimXpdCk47BWKZFLxQl1VmfjDn8Hno5h0ksPKKcaN8YUbQ70Un1DyMKijpZYDU63j0K28eD5PHG4wxSysYbfb1RUVsuNFaEFQUKRTx6QSGaWCftrE20dThtqxThk6KaTRdy+5snpIbeNHy3M8PbIFQaCtc/AdmyTkYnzBAKNw03288NUnlS9T7b+LoxssianSVRkbOO9FO/PtnR+jXBkC5RXV+j88pdrQkMAwwN95Isl8pnqcx/22BmK1OahiYJwGi2tzjjjhXFmsHxO1EuaOwn5jQSuyOGXv6Zp2Gy2usaM6HTgPD+MbbjncEWvrxscjQeTRFEl4j1dg0WfvIc8cRV57CLK2CXESBfqZz9EcLmw1h8h+epXaB1kZnKaizevMdjbzfilCwzc/Gk+ji9hWSamqSFJTiTJsTuYCYJFpdw8iVQQhIYlqaZhsr40S89wbY8cye4kMHgVS4DM6hOya5PoxdqSUldHP6X4gQaGO3glidARzSwDkS6yqdPTp2mHQLiTXLrxvkuGhdok5JDJ3D5ROOgZql7fS9M/FGB5vtbITG4X6exu7int9Clc662dROiaxvz8FKapEww3L8sSJbHGUFI6DydVKyE/SleI4sO5pttqRO7dH+F+6SaRVxv3Fjs3OsTadpy19ecnp3DGGS+Ss6TbzwmX10sqtkWw43S8K8Vkio5Lh5NLA4EAG8vrOHUFK1pfvbOQV3G5Ws+hKWs6thNWNe3Hqqjos/eRRy8hiHteLNHjxXarWikhrc0fuR29WGR2YRm311sTsuiwK6wqbrYyT/Eoe2rCdnsHxeIinf0TrMw+JNzZj6zYsTsPJ0Tqqlq3y3CllGVrZZn+scYl0c5AJ85AJ5ZlUUgsYsQL+AeqrRCa5bF02Fv8TT7XWXLz+6BZM0rTVJGkkxu9Lnv9xG+bQ0bXTAzDRJJEdNXgSJcVsJaLMXig0m95aY7htoTi9s5bdDox8gUkT63BrXSGQBQo3pnBdbP1Ds6VpSWUaORQ5V49zo0N83R6jmctMg3T5A8TWeyCiCIKhJo0Kj3jjC8aZwbL50R3/wBTD+7jC4VrpLSPy0HNh2eIokjPYC/qcg49VkTpPPySi+Uq9NWZBTZiPV1mINyat+MojK01zHQM5eKthrLl+to8cvdg3e/2s7Kyxuj5iZo4faqgYloWL4X7uJMJ0ikpSLLAWr6EQ1Tok32oapzuwXMsz9wnEOmua7BEB4cPfba5PINpmAxMXG7pXAVBwBMZppJJUkyt4gruhDEEEdPQEVtM5qxHq6XUp40gVsXQ6pXlS8IR/Y5OqL/SCkNjIeYm4yg2iVy6zKWbR7d2yKslAvvugXwuh8PtbtlYMUoVxP2G8cQA5UezuK4fFn1TokFEh0LhzjTum61Vi6kzs3jf+XpLywJE/V6WNzZxhyN8nM7zjYgPZcegX2zQPfqMM76InIWEWqBcLu92RT1NHA7HqSiGmqpe1SJp9H1RR4k6oYFXRLcsbG0MdkZFP7EqrKWW0Z7eBVFCOXejobECYGytIHUPHbnNilWbVAiQKqpkdtRPr/lcmEDRMLnocRJWJNZ0N4aRR7HZGb30Cmq5daVay7LoGW6uPFoPuz9EKb5Mdm2S3NoUKAKFE1TcRHr72V4/uirpeWBZNNQQGnTaGXY1yRc5pQSKfDnX8DtJFpm41MHweJirL/citXCfi4iUtb3cm9j2Oj3dreVPAZQnF7Ff3uvzI8pSUy+Y4HSCpqNnC5hNqsLKT5+S/e53cb7WXhgt0tmBmivw7laCn+kI7BorZ5zx48aZh6UFCoUC4SPi1sfBsiyUUzCEknMrBCcav1DNgobS4cIs6ifeF8CWU8RKFtFVg2jQSfQYuSz69EOUyy+fyvE8Q6ozAIqCgL5jFEqCQL9j73pvqTrjLjul0l6zO/MF9eYJjry6G2LSyyXiqz/CS+thgf0oihND/5xmyse0OVQ1iaKcjoZPyBNmZXuZ/ujA0Qu3wFAwynI6wUS0m9XVRTzuwzkviZ3+WnXfC3XUly2tybMngOh1UpldRRBFRI8TI5XH/fJ5jM0lCh9/hBjtxzYygu/C4XL/VhibGIUnU8BZt+Yzfnw5M7U/Rw52C26GniihbTfoAWJZR2pDWKbVsDS4XWdJwbKY6PJxcSCIVTGYWk2zHGvcfO4gxtIU0khrYRQjtoIUba1DsyhKVU2ZfeimSaO8T3mnmaPD0Ue5vFrdRhthmZM4mfbnw8gOJ7aQF8M4vtHh9Lgp5rInOKJjcsyLkMs/xett7R44isHOUcrq6SkuzyQ2GAlF2d7eRJZkOjrrCyfW844a2Tyi+7CYnqUfrlh6hihJGANR3DfP4bo+juhyIoV8lOfWMFancF65iL3Di62r60TnJZx5Vs74MefsDv6caVlfxIKGI2+TQUOLl5D8NvRECTlSX5W0nUlyxTBx73Ord0TcnOsL4LbLLG0fNloszUCbfog2/QBt+gH67BNMVUd0taaQqq8tIPe3FtsXHHasfXoz2a0Yw16Zic7aGXJC1bmfK+LaOQ9RVLCs6gy4nfH3oLbNSQgEXiad+fTY6wejfWQSG0cveNocN6xjWQ3zro6DqpdRT6ldgIVFKrFNIrZFV3fjvkT1tGjKMys4Jg57epzXxig/qF/GnE6nsSyLbLZqcNp6wjhGe9Dn7qJcfQtl5AqCJKDNPjzmGVWRRYFSRaWsGTzdyFJSX2yn7zPOOClnBksLGM8pTOB0eygVWvNMWNAwB6XRmGHkVESbiGiXMTLqqbQWeC+V4zX/4YTbsN+BQxZZfXQHY30RyzTRZh5jLD5GHj2PMnEVeexytWR5/FLL+2vnkK1SGcHhIJHO8PTBI3KiyMLaBotz1QqjQrHExw+eUCoUOO92EMllKE9NY+lVY+VZM8VWjEjDMDGaufnbRBRlLPPk99nzEvNrvL9j5mC1+MOmNf1ItV+A833XWIodXUnWCqIgUioWOX+pcXsFRVFQ6nVr3vHaHURyuzD1+veLYRh4vd5DBrB9sB/BVg23ysOXwVQx4uttnEktA+OjrM0vspEpc6Hbx1r6rGvzGT9enBksLXBagnEHiXR1E99sbVYs2iVEW/3jaDRImSUdyWfHyKkoHY09Gq3aBFUXuIDcwLXsslWwd0cR3EH0ucfIQ+PI41cRdtoOtOuSNhJbiP7m/WD2I4gCK8ur5GLbXLh6md5ohLGxEWRR4uHDJ6xPz/DKhXGiqSTm4yeYpRL2kWG09XUcjn7K5RVC3Z1sLR+dALu2eJtQ3+neF07XIPn8zLHXD3X1sL2+copHdDTCEWrIjRAFBcM42hBJaDo+WSJzhHFYqBRPnAj+jGjZj2Jvnlvm8/nw+WpzcEy9fvJ78f40hdtPUbrqN05VFIV4PI7TufeMGtubSJHaUKgy8RLG6vHF5gRBwNp3jcRT7rJ+xhnPm7Ok2xZQFIVsNnvoBXVSZEVBb3GWLnnrv0Ar2Ty2ejHznVJTPV3GyKjYBxsfez1zZ7FYoWQaXPDslXd+mi3ySqBx07Zkap3Bvqr3RPRfabhcqxgrU9iuH9076BmRzg6MUongYD/JjQKyIuKLOOkbHmS/Y98+UluibOl61cNhGdjtfkxzYzcJtxGmXkYQGuukPM2XCCoyXa1qqQBu1xDx+PfxeI6XfGt3eDG048/Aj4NlHc8r5HB0Uy6v43aPNF1OQMAmCJRMq2mTvrXEHLYTarqkkyVSiSI2wU53d/Ou7PVQZ5ZxTNQpvzct3C81Tpb1+XxomlbjsRHDHehzjxHDHbULt2hk6KqBXGeCY5Mk/E6RpxtZhiNuNht4fc4444vImYelBXw+H/l860ml7XDSqpT04gaBocNJgUaqjBRyIMgiSmfrnWHvZQt8L5HBwCKhGRT06vGZlkVc0/A204w55XCEFV9AvfculU//BG32IerDj1DvvYuZqD8o+/w+gl3VKghRFNC11sIVBz0/oiQeGT4rV2II1PcuZDSdqE0hoxssxWZ4tPSgoRcstr5McWvPK+JyjZDNPW3puOthcyiUi62XZp+UQHiQxFb7Jdml0hoOx9Glwm5JZL2iHWn4Xei/Rmewj4Wt4ynHrq9myOeqIZlGisZHYVY0RGdtGXfhsycoXUdXGB4MLwmiCIZxKIm8lWeskKmQT1X/DjIwNkJsaZkL3T4cinQmzX/GjxVnHpYWsdvtR86622V7cwNfMHiibTTSX3mmjyHYJMyihug4+qcuGQYZ3eBr4epcdthp508SWWyiiAC8EWgsab6dWCEc6m34fduU0tgnzsNAVXPCTG8juH0Iih1t8Sna8iyCKGCpFewvv1N3E2KLaryWZWEZBs9iCq28xB2OfpLJ9/D5DnuSZFGgqBuYhk5JrTDRM8H9hc+4OnQTcZ+S71p8ma2tLBfHu8iuPsUyDRR3ANXawHSPI4rtP56hzmE2l6bpHmpfH+Y4OD1eUonFttezMJFa6FDeaVfobNFLtRKfRxKPF6KqlHT6Bv3V3PZjGiwH1yp89hjHxVEk1/E8P/LoJfS5xyjje/eYpetUPvsetosvI7jqP49a2SDQ6SK5UQBqDShBEBAsE8M0kc6qhs74MePsjm2D0zRWALLJJB29jasQWqGRkuizT82ijniE7L4AJFWdP4pneSu49xIUBYFvRPx8JeTlrZAXf5O+NuVyHo/7ZMZXDet3of+VvWMJRBGU6stXGbqA/cZb2K69idQzTOXTP8HMpcCyUO+/h5mOI8oCNmdrA75tcJDy5CTPhpzk5gZa5QgvhWnH46nv5ndLEmXDIhWf4VzvJWyKg8uDN3m0dAfNMLEsi0wxS76c5/z4BIm0gq/vAv6By0g2F1IuQGr5eBVDp3WPJtZWSLeYXxXuGCW2Pt3mHk63l1YynybgDjLR27hFQjMGR4PMTyZQFAmHo/UwXg37LJbS00WUvu5jGysAQp2kXvutr2F76auo0/fRl6fqrifbJArpCoq9vvE2PDbC4vzisY/rjDM+L84MlhYwDONUFGkPciqDiwDbj+fYfjJPYSNJdiVG7NEsm0+myD3ZQA7YMTLNyz0t4LNsgW92Bk/dKDsRLRyL3DuK7ebbGJtLqPd/hDR4AW3yM9zqFi5fa6J8RiaD0r2X4Dhx41U2l+ebVtxIsoymZRpqpwRMlUFfYPd6ypLEpYGrPF2+x735T8gW4pzru4jLY6NS0HbXs3tDBIdv4g6OkN86XtVLIBohuXmyhneiKGLo2tELAnanC1GQyKfb6Bp93OqiBsgClNTGnaGPQhRFBs+FWJptv/O1aZgYxTKlokFivRo6tioVbF2tJ4w3pE7ptyAI2K+/gRGv/xt7gnZEScAbqm8spfJF/G7XzrZOfohnnPGiOAsJtYBpmqcqzb84PYmmqnT2ti733YjoxWrSoqkblLNZbC4H3r4ouqZSymQAsA7kcpQrOveXU9waDSMisKFp9Phaz3Opx6mGwg2trTepIEko49f3/n3ly5ipGJVPv4vo8qBcfBV9YxGpcwChTsWXmclgGxqiUqgOVorNQUffIFvLM2iqStfAKIq9NrE5Hdui93wUXU8jSYfVQ7czK4dm+5JkZ7z3ArIoocj77idBwFANpH1Jkg5/J5ns45avwX6c7jCp2BRwfKExy6Jus8dGRLpH2Vx5iKw4cLib30up1GfYHacYPgR87gDryRU0Xa29tm2wNJVkYKy5l9AyDEpP5rB0CwGqpeiCCJaFplkEvDZK+eMbTodoQVvGyGUx5u+DZVXDmZqKKCtowSjKcB2Pk2FgnCWvnPFjyJmHpQUURUE/xWz6nsFhtIqK29u8zX07iLKEKxTE4fchCAJauYxtp4GbYJN2vQWmaTK5nuWloRDzmznMks6cpXPZezKD5VQnaksfwMCXj7266PYi941if/kdpMGLVO6+iyCYVD74PcxiY0VUh6OXQqHq1bA7PXQNTtA/fpnY2jxr808xjL17wBsOYmFit9eXOhcbiKI5bc5DA+rgaJDFudShZT0dI2TXJo8833rINhGtcvx7VrHbd++fVunqv0JiexKtiaCeqiYAC4979NjHVne7uk5JLSAdI+/nGTanjNPV3NixDBOzqOG+cQ7XjXO4X7qI++Z5zHwR0e3ANC0kScQ21EPp4fFLkM1EDO3pHeTBxqKJcvcglbvvYm5MY7vyJWzX38J+4y3EaA/m9ipSqL7BGu2IkM/myKYzlAyDdKH4wvV7zjjjOJx5WFrkNMXjbHY7567fYP7pE7x+P139p9MDZT/lfI5AVzXMIQiABZph8Gg5zdXBIJIkIgkwvZllrEmp8ueCaYB8zDyCA4geH/abXwFA6hpBe/IRugG2q6/vLiM4XagrK9j6+7Hbo5RKyzide79J78glTNNkfWGSvtGLFLIpbDYHpeJjCL1Rd7/tDACSJCJIdcTGdrw6hqYiKe15DaI9I2wuzdE91JpK8EG84fbLegF6Bm+wNn+H7qEbdRsN5nKPCYdbL1VvlYWtKcZ6LtZVn20VQzUoVzQcTZJ8RZuCYDv82pTDAbyDfVQKOm6/HVNxY5aPp7yrzT5G9AZQLtxsupzUPXSoKaiRWAe1jPMnv9V03dHzEzz84COsoSFMr5tUKtVYzym/TfSgrIJlgeICZwBkx1ls6YwXwpnB0iKBQIBMJoPf30wNonUkSWL88hVSiTjzk09RTJ3O4dGms9pMbBN/x9Fufsuy0Mrl3YoUKWCnsJlnXtO4PhzazasY6a6ey/HaqR3c5yls5FQ3VAdBQLn0OvryDMb6HFJPdZavdHagbcXQUynkYBDTVClXNnHY9661KIooNjuaViGXShDtG2B5+feZmf27jI/93w/tymhTn0QU9how5nI5BEHA4/Hg6Z4gu/yAwND1Nk9V4pQDdS3uV6B76DIbi3fpG32pzgLPR4RxrOs8DxY/4crQy8ht9IPaz+jFKLNPthm/1NF8wTr5bM7L1XvJ5a0mhpfuT+K8eb7tY7BKRQSbHanzeCEzfWka+823W1o26nXT1XP0+ySRXobQgXOxLNBKUEqD3iRBfd/zbKkV1LyIYHftfifYbIhuN6LbXTdce8YZ+zkzWFrEZrORyzVuY39cguEIyXic7rFzzN/9lOHrL6HYDyfLZbdjaOUym7PTdI3VnzXnEnHUUhEQiA7tiaMJksisaXBt4Dkm1Z7WZtfvQPfVU9pYfeSBcdR77+4aLFA1WtSlJQgGsdnCFIuLmGYFUdwrC432DBNbncXQDbbW/4Shof+CcvmwsmxZLWOT7Ic+b0akw836SpbeAf+uRLvH40EURVzhIfKxeTwdzUXWDuINBUhvbxOIRtta76RIkh1PoJt0Yp1AuH7jwNPfp4QgSJTUIl7n8QQeN1azuwZH850d7cURFQVjO43YggbLfvS1BeSRE0whBBGrVEJwttar6/j7EcDmqv61iLH0BDkcQgpVDULLsrBUFbNQQNvYgP1ebMs65LWZ1y3s3bXqv40wjQoj6nb1tSTKICkg2fb+KypgapQNDVOSkUUZSZAQhaM1mM74/DgzWL4A+EJhRFnGF46ycPczPKEwwZ4+iuk0pmkgihKKw46uadg9nobb0dUK4b4B1HIJQ9drpPwv9/iZK1UYO0GZZSNK5QKK3N4A3ZByFtzHC0ecFLNYxEinkQIBXK4hCoVZ3O6x3e/FnYGqXMrijopo2nZdpdaV7UVGu8cOfd4Mj9/B1kYO0zARBKGmKs3mDZDZeNS2weLxdbKxOPXCDRaAQLiHZGyF1bk76HoJX3CAUMfJk8yb4XX40Y3j5+2Uixoj51q494yjK5ycV8cp3D5aNM4yzBotJUEQTtRV2YytYvYMIjnrKO4+J/TVGTLhEB7NQrEERH/9czbzKZTevcmWIAgIdjui3Q6hoyuqbNOzDLtae88sr85C78Wq0WMaYKg7fxqoBTBUMsU4SaOEO3IO0zLRTX03LCYtvUdPzytH7OX45MuLiMHTzeM6SGprhsXbbi6+MQTAH/7+P0Qc9PNzr/85ALLJVexyi++UHQNSFMWWvPzPizOD5Tmh61VvjCwfnVjr9/vJ5XK4QyFyqQSeUBi1WCTcV/uCFyW5rvdlPytPHuIJhTFUlcjA0O7nsiggnm5qLACWobO+/oTRkZdPfdutEC/FiTiPYeDUmUU5LlxA29rC2t5GjkaRlQCalkJR9ipHYqtrjFwZp6JpOBz1vQcW5rFE3wbHwixMxvF1HG6g53Afz+gQJQFdN5DlF+9uD3X0E4z2USnnSGwtUCjYkaXqjDydWMcf7EZoUdyvFYa7xplef0LQ0345sVqp3weoLi0bFDtChMU8gqt2omGm4xjbO2XJuorUO4LoC5w4iid19CLYDzcnPQmm2lyTSF2cJLNYIlXO0yt4cX79zzXYkInQTCn7NNnvKRElEJ2g1HqdEqk5Rka+Xnf1VU83hJ+PQVHa+hTF0YXdNXSi7axMf49Iz1Wcnvrvv3vb7zP29W8QDFY9Wg9cKa6kCzx4/x/z2jf+LtntGMG+9jygqc0X2/rjIGdVQi2iaVpbTRArlW0qlVhLy9psNnRdR7Q7CXR04Yt04AlHaiqTkuur6JUKdldjF6wnGKZ7/DzBrh4UhwPzQPffVprIPSNf0VtqQb+wdI/BwTq5CvtYya2gGS1oeiTmwN+6kN5qbhWA7eJ2y+sciSDsqN6C3RahUonVdCQeu3YdS8jXeF4ObeKYhuHtufcQAhrxjTiRSASjrJF4kmDj4w3Sawbl4uFKoqOI9AyxvXo6XYyPgyAIOJw+eoeukU2skUtabK/PUMhssbpwl/XFh6eyn5XtJabWnjDW3X7eCIDNLmO14DkBKN1+iJZIt7bs3YfoU/fQF2qF9YztTZTxy9W/Czcx09toMw8x47MnyuOy3fgK6pNPTjUXTHEHqOTr33tmehM12o3vpa8h3nobKVy/au40OM30NkMtotgaG3aCKGGaz6fPkqHnsIdb71jfCIc7zPyTf8fa3LuHvsskFhFLSdymtqvc/Je/9H9CsgSuf+n/Wl2ozdCXoes1St2fB2cGS4tUKhXs9tbDHoIg0s50KRwOV2O6TjczMzMsLCwQi8V2XZSCKO5W/TRCcTiQd9Qx1XIZ88ALOGKTSWqtJYNu5ypHtqBfXn1Cb+8F5CYz00Qpgc/mYy2/dvROU0sQab3xn2mZRJwRcurp5RYJoojo2PNiud3jFIt7A77LE8Ruj6Kq8YbbsI45Tb44cI3ZzfcpqHcASEylCF0I0flyJ/2vXCQZa+EaHkCSbFinrCp7XLoHbtA3fBO3L4rH303/6E2CHb0kttaolLIUC2m214/XrTpfznC+79LRCbfpFSjVH3xbreySu6Io4cCRy1nFHNrsJMqNN5AiUbQntzELOz3JDgwW8sA4yvgVbC/9BEz+Pmw8aOlY6mG7+VUqd35w7PUPEug7Tz62XPc7fWkW3/BVtgpbCIKA2DmAev9HlH/4O4cXPqHFoZptJLIfIUy4svEZvT2NvcIdPS8TWz+e2vRRCKc07EZ7r+FwRVHLWdYX3ufJZ/8UAF0rM/Pof8HpGkKb32IjW+2KPtoxxmgkir7TJb3d95RaKmJrMmF+EZwZLC3i8XjaaoBomOW2O9l6PB46OjoYHx/H7/fv9i9ql8TqCsHu3l3j5bhYltW034ggCNiPcD8XtAI+mw+zFWXTNi1+m2RjPj1P0NFmS4Am11SORDB2BPeqhyQCYs3vkMs9JZu9194+n2Ea1UGzDl6nnwsDr9K1E9tWPDZM1dwt1XV7w2QSrcnl78fj95NNtq/g+rxweQL4w9U4uNMVIpueZ23hMfGNWWTZQamQIZeOUSykW97mSNc4k6tHCO09y2UoJBosIJCKF0huN9bqAbB0DT2Zbb6rxDqKPYf3z/xSdcveIMrFlzBWZtAef4YUbeCKVxxw4WdBdsLq7ebn0wDR4UT0h9A3lpouV+9psyyDykFjXBTRSg0mBaaBaLNxIXyBYf8wcvcQtmtvIgZOPw9NatEDViimsDkah+JNy0TXK029BYriwGjFK3wMZFsArZUJXAv0DL+BphZQyznSyUnS8QViq59y+eX/nKJnFMXopjdQDYUlth4R7X2FT77/a8fal1oqYm9Tm+m0OTNY2sBms+1WcByFx30Ot/t4GhgA0Z1EyV1diRYNl2w8hicUamistGr+9AQcTG7m6A/WrzZQ1RJCC+Wj3e5uniaf4lKOuNELCXC0V93R5e6iz9vXtsFiJlYRAvUTx2KPH7N2wDB1OvsolfZmmLncQ1yuMZaW/mlb+wUgOV+NpRfrGxCqruKUq9fcN+Alt7I3MPrD3aiVIvlMa6HGZ/iCXRSypxg2O2WGz73J0PlX6Ru5gTfUyebKI0qFLAv3Pm55G3bFyZH9iUyjWiHSYCIR7nRTKOqk4s1zNmw93ejJxl499e67qE9uY7vxJsKBQVE5fwPl0i1E/xH3bHS8WjJ8TJSxa5ibzQ2Weu+CYnERQy9gmrVqvYqzNgfHMgyK6+9SUavH2Ego8RlmqdBG7k99pBbzXxLJdboijbWtREFsqUnmcUO7R2GPXkdNH08Q8iBOd5hCfoVI71UEZO5+8N+wsvBDVqbj9GkecpF9OY+WgNMdweOdOFarGcu02lK/fh6cGSxt8Cw5thUE4XDi5POmlM8hCCJ2V2OvR1iR2FaPnjnYZYkL3b6G4Z7Vtaf0dh5dCaNIChfDF+lyN88sL079EM0/dOT2DmKT2pdh19cWkftrE+oqqRQLH3yIr6+PcH9/ze8sijYsay+e7XKNYhhFnM5+isV6rvImv7sggisMhfoGhKYVkXe0SiRFwjRqh5Vozyjp5Dpq+YjmjHUOyWhxhvp5IIoioighyzaGz3+ZSkYk0j9GYrO1F3s8s0XIc0RismyDSrYqdFaHQNBJ34AfsY6In7G2gD77GDObxnnrAtp6/T4+6uMPkUevIPqDCG2K/R2izgC/nl9nNtWigq5Q7e7cDoIgYbd3UalsHdjU3jXRcysU1v4IR+frWKEuNLVqVMdX82TjJaxiFmy1Ex396UfYLh1fvRraEe88elgTWkiKN7TmnraTIIinI4wJcOPNX2P6zr/n+pv/N2w9f5Vu1xU0zUcmZdb0oor2XqeY26K7/y22Vj5p2yA7bqj7NDkzWNrk86jR11X1SAeLplYo57JHKpT6FZmMfnzV3kIxw/zCHTq7J071WpQd3ZTj62QmPzi1bTbEsmpKRwtbWyRXVxn+0us4/H58Ph+WZdUYLftf4k5nP4HATUKht8gXJtG0vRDSVnqDsLdWeCxf2uexkRTYuAeBw2WnZbVMJTGNt6d54mjf8HW21h61c8Z09A6xvbbQ1jqfB4ZuMPPZYwJdHXQOjFJs0TOUyifpaOA1q6HzEgQbl/wauomum2QzZUrFqmFvZlJYuoo8dgljc4XMv/sexc8eYVYO9wyyKiqiL4jtan0F5Lao47XQTR1RFFsLsQKl7/5r1Ab5LOVErE7I2aJQmD5UAVfS4xQ33qO48R66msEz8NOIkoIZ7ODTez8iVlSxOSR0zURfeoo8dKCHkSw3DPlapkU+dbTnun+wj/m55gnklmm2pAkVCI6SiDUPIYpHeYVPgNVGQm/BMJgtlut+Z+405tX0dYStBF++9Q6pgTepjOhEOjfpduwZt7Jip6P/BpnEMt2Dr7VtsDwvj1M7nJU1f0EpFAq74SC5hcaL2VjsUBl0IwT2VFXbJZFcZ2S4uWT4cRAE8A5dIfP0/VPf9kEOvqPTa2v03qw9J5/Px/b2Nt6dfk+y7EZVq6EYr3dP2Ksj+hNsbf0+kcg7SJKNdD7Jub7aCoBPZ77HV6/+QvUfgYHqXx2ertwlWM4hyjLJySSSS8bU6g9MLneUYj6Fy9NaOEyS7dVGfV9gFh/MYFkWozfO77qeOwdvsDr7IYIgEOm5iL2BKJwiiaiGif2E5duSLCLJAtvreXTT5NylDoyVGZTLO5oclonvrasUSpMIlg5Un02rVEJ98iGn2lWrjlFiYSEJUkuDhzx+A+X8y1i6RuXT7yIoNsqba5Q8nTjLi5TXFtG1N1Bse8UELtcwpqnvKCXvwx3FdUDQcXn6T3BHzuM0I6R1nQ4DRFHAUlVEp4vlz/4Q905JrVks0cj/lY4VsTllygUNh7ux58Hp8WAsrWAaRsPQRDq5gi94dKVhwNfLcnqBpio5igurkkewN9a+Oi6Hri+Q2HqMJLgIdAzXfL5Z0ei2KWxWNLr2tY1YmvoulVKScm4bt2eQ1cX3GBsa41LUSyVZJu42YXsKeveqOPOpdYrFRfLp9suTzzwsP4a8qCZhpVKJYHBvMBKExvsupFO42mgZ0O+wsVw+XkfZ52Fja9k4ksPz/Hawf19LMyg9e4ZdYm6O8Eh98SSfz0c2u5dDIggKhnE4FBON/iTJ5A8B0M3Dv5EoyMTSW03vnUQ2gaqbBHtfoZKrYJkmDq8N70D9ATrcNUQ63l7insvrJp9uvzT6RbA2tUx0sIvhaxM1g5HN7qFv7HW6h18hE59nZfpHaJXDs027IlJusQLuKHTVxBuwMzgUYOXjD6qhpB1EbxCrUsT7538V9cE+41qWMbMp5J7hOls8JnXyDPw2P4qotDTZEN1eBIez2k/r5XewXX8Lraebzje+RkkUcF/9BpXS4fu5noaQQwqz+fQzivHV3c9kmxuXv5vI5csknz5GUkSc3p0qxZn7eMLdhEdvEB69geFpbEQIooDLZ6OYPfqdNHx+goWZqpclU9SY2aoN0dv83WjZ+uG6/RhqCUFqHpYJhEZJtRp+OwYHJxCS5CK2/tGh5QQEnJJI7oBnXJIldK3AwMWfYvTyL5IRN9HUArKi4O7sYVDXa4yVYiHO1IN/iijJFLLrXwgDpF3ODJY20HW9LS2Wk2C32ykdeJnUe0mlNtYwDQOnt/WEVZsoon+BurOm5+7hG74CgOTyo2UbVXGcHDOxiti5N6hohQKOQKDusna7HVVV0bRqaMDp7KNYXERV4zXGhyhKuFwj5PMzKJLI/flPyZcy/ODBH5DKp+gODfFHn/w9jCaJbquJZcpqjlKiQilWopysoJUN7L7G3jWny0cxn2n4/UH84V6y6fYSdl8E2UQWBAu3v3FlhyiKdPRfp2/8y8TW7pCJLx743olhNC7Bb4eJyx10dHvJZTfx+52g7hlIUs8Acu8QgiwjD52n8ul3qXz2PYytZaRw16FmhMdm7vvQd1jfKOAI0OM5XrsDNb6CbScZtfNr/wlSKUk21ZoBG+7qRVS6kRQb6aUHZOKLON1hlIxKNlbGZRp4Qw7sLgXbtTdRsSMp+wT8mrxvRFEgvpLHFz5ahVve9/5NFlV6Ak62snu/j0uSKLeQ67IRu0dX57Wmy7gdQYrl1p+vdrCwdioQ93B5OiiXDr/7PJLI43yJkQMqv5Gua4xf+wsEwqOolTyB0CUmb//W3gLDb0H03O4/JclOJZeks/dLRHquHkvc8vPmzGBpg3Q6TaDB4HbayLJcIxxXL/6bTyZw+QPH6qx73HikaHOilk7vIdZyCezBPX0Zz8Bl8gt3Tm37+zEyaUR3rXv3oAz+QSKRCKlUatdocbtHEQQbhcLMrprxs881LY1ReUoqH+PR0m224h+RyG4BFpdGf6nmZXsQRTQRRYVyrkCOWXrf6MXT3bxkPNgxQDpRXx+jGS/KS9gqqfUYvROtSckLgkjvyJcwLYPV2Q/ZWrmLZRnkyiX8p1xyWSlk8J2/gXL1tbrfSx392F9+B/utr2GV8yiXX6+7XEtk1mDmj6uGytz3ITRc7YR8SlimSXppEm/Pnjex50vfJJtJt7EVAbu/A1//ZTIrSzilTjAtLg6F6FP8lLb38o1UAxyRCHpFY/HONPZK43vfF3ES6fMg29qfDEqiQGVf2LRalXX0/a1pZZQGydf7ESwTVTsdQ7hmu6KMZdYWP9gcbsQ6RQQddoUrXhfSIfXrEMpOcrPL28HK0seEui6Syz2pu0+7w0uk73WmH/5jntz+TTzB59sq43nw42difY68yMqfQqFAJLLPEKkzyFRKRTyh9pqrPUMSwLCsQw/BUYS9YeK5OD3O0+lanZ6+TeTmN2o+s0eHyC8+wDN0uAmievd7ICsgCAg2B6I/irGxUE3qe8bOtRIjfWBomPF1LLWIZYDjtZ+u2Z6hqnul4w3o6OhgcXGRwcFBRNGGKNpQFB/5wgyefa0XgsGXCQZf5gIQy8Twu6OM9Zxneu0Jbkeg6T4uDr7E5OojJEoU8wkef/q79I+9ii/YXDnU7e0gnVgjEG6tu2+0Z5DY6iKd/e2HLrbXl9F3vA2eQBBv4HR6FB3HgApGRwlGR1ErRRYf/wlmoB/pGBVjzTioFN0MZaz5bP1I4tMw/o2jlzsGpdgS6w8/o/NabSLw5L27dPa2riz9DFEUGbjxFTbuTRKMdCPkRQKXx4k/uoNzR45ByxSJz28iKTK9o10Y2801rI7TosHnkFmIFzjf5cWyLOaX7mGTHXh9e/flam4Vu2Qn6tr7zNLKiHV6n+nb20h+P8K+nMHeobdZWfgT+kfeafv4muHseIVy7FOc3Qcrp443mVBsLjr9XiI9F7DbG09gBWS+9ov/mq2Vz3C62hs7vghJt2celhYxDOPIge00OWQYHfh3JraFL1pbjdIO3XaF9Up7wkhaOc9KbIGejtOJ0+fm7+Mfv3XoXF0944hOH5mn79V8ri89ReoewXblTWyX3wDTxFiZxHb9bWyX39j7u/ImtitvYpVyYFrYrr+N/ZWfYWM5RmFjL9msGI8jtTgrd7vdh/v72Ltq9Fn20+HvQBIlTNPEtEzMFkQEe8NDbJeXGL/yDbqHrpLPbh25jj/USTGfQG8xf0OxOTDbFMQq5jKszD7GGwzQPTRB99AEpUKBtflJdP344lqWZbE+s0zncPuD5jNsdhf9594kdUyRtXpsrc6xPPPghXWafl5kpj9l67M/xNJ1Rr/+Z4iv1eY89Q4NUy4d9h5YlkWiWDpsSB54JXVdO0d8bRnTMBEd8u4ClbkV/JEuei8O0TXeh74cwzZ4OpL9mUQSdzAAQNhj50K3DwFYWr7PQNc4/X0XCPj2BmzDMijqtee4tXWfzs7rNZ9Zuo5ZLKKurtZ8jiAgSsqpeyUFyVZXWFQUHOg7Hp3ZB/+2rW32XvwLfPSjv9fwe12vsF3+f/Pp9/9fVErpH8uu1GcelhbRNA3lhMqx7XCU5oCuqTXZ/e1iE8W6CaLNWFp9zPjYq8fe50G0Yg6vr36jOlfnEJo7SPLudwhcfhtRcWBm49iuvLm7jHK+eTdVZaTWQ+MevUQllUTN5wmMjZOYn6f/ldY6sgaDQWKxGB6PB9eOPLUsezFNlXJlE4f9cEltb3iYlfg86fwG4GApNsdgR+OGal6nB0dolNjaNF39rffE6ew7T2x1iu7Bi0cvDNhdTor5LC7P0XlP2+tLYFn0j9VWPnX0DmEaBrHVOVRVY2C89d4o8ZUtcoks5UIBm8NBz3h9ccJWkRUnw2M/yWd3n9A30Mv81AxOj4frV84d66WslUsMjB/27v24UNqYI7cxT3D8JfwTe/LzB8dcbyDAuw/m6NckHC4HnyRmGQ/0oogKHkVmOpHi9f6ehusLgkD3jQtsP56hK3gBsFBXt7EsC8fYvvVMC1FpLdxjqSra5IcUyhYVnNWqI8vC1DQEWSaWSDL01dqGhWsbU0S7z6PY6od4jAOeMtXUcNhqJypGNosUDKLHDud4dXTeYGvjDl09zXumnQZdAy8zfe9/xR8eY3PlNkg+Ri9+vaX7eGv1NqKaYvLJp5y/eLjtgCDaKJbewGUrt90T6IsSRj4zWFrEbreTz+fxeE6/xO0oDF2r8e7kEnF84dNxx7dCuVJiZXuJ3s4WW5G3gJpYQwk0n3UpHj/Baz9B+tH3cQ1cQ2hZOKo+pqETunSF7OICT3/v9+h/682jV9pBlmU6OjrIZDKUSiWczuoga7OFKZc3UNUENluti1XVyyzH5pAlO0FvmLXEImW1jNPuZCB6+FqWVBXZpkCbhqQk2RCE1tcJdfSzsTjd1GAxDYO1+aeEu/pweQN1lxElia7BCfKZJOnEJoFwcx2USqnCypN5ogPdDF8fr+YOtXmujeiIRAkFwzyZnOP1118iWywzO7/E+OhQ29uy22xsLU2RL+RArzB48VXkF9Vl+ISYmkZxe5XojXcODXLuQIBsPIYvUvXMVsoVFMVObzTIvflZlJyMv1vlfLia2xDLF7jzcIEeh0KwJ4JZ5/mTbDLe/k7ij2eQvT60zRjuW8dr7Kc++QAsC+XSlxFmb9N1rjo52u2nJgh0miYPl5a5Ojy0u55hWbgbGCuWZe0qRzdDCgYpP3qE5D2c+K04fM9VRG4/oc6LFHNxTFPnlXd+ne2NJ2yufIpWztA//nUWJ3+f/rFvICuHJ6ujl3+BXCHNYuYuY/pV5ANhr9/95AH9vT/PxNBrFLLtlTVXCgXsn8PYd5C2Yhz/6B/9I65evYrP58Pn8/H666/zB3/wB7vf5/N5/ubf/Jv09fXhdDq5cOEC/+gf/aOWt//tb38bQRD4xV/8xXYO64Xwot1n+6uR0psb+KJ7g7tWKaM4jk4YO4pWh4r1zSmcioLLezpGUiW1QXZtDm/f0Y0OBVEkePXrZJ58hNR7/FYHqSePCYxV1/cNDXPxF36B1YX5tiWq66kdOxzdmKZKsVgrzHZv7j0ivm6C3giWpfP6+a9xru8SxXL9eP7K9iydjihub32vUzNE0Ymut65+a1lWw1mTVimxOvuY3tGLDY2V/Xj8Icr5oxWglx7OMPbSBfzRwM4xi4gn1E3ZjyyJDA/2shVL4nc7KZaOV7ofHZhAkB2MXrxF79g1klv1ez+dGlq52jLgFIjd/g6hS2/UfV91DI2QWN0LYd6dXOSdl84TtNspqSp/9ss3eLyxl1AfdjrxhUN0jvUSX9yic7yaJ2UYBpubm7tVjO5ICKNSwZYzsbQ691Sr707DwHbpy4dafuzPHRREsa0mikP+Ibo9zZvGPtuH4/JlbEND9b9vI38jVoyxnm9f5wSqz+Xi9L9lZe4/kNx8TO/QK3QPvELP8FssPP090tuTDb0joigzNPJl+kSQ6pRsu9b/G5Lz/z8svf3nopzP4XB//gZLW9OGvr4+/u7f/buMjVUl2f/Fv/gXfPOb3+Tu3btcunSJ//K//C/5/ve/z2/91m8xNDTEH/3RH/E3/sbfoKenh29+85tNt720tMSv/dqv8eabrc96XzSSJL3w0BCAw+MlubayE0+VsDlO5kJ/hk0UKBsmjibdlgFkyUZfZ+NQRrssPrzDwK2321vJ3oV4xAy+GVqxSDBc6wEZu3SF+aePGbt0peF6hlHGMAo13hO/38/W1hbRaHTX8/XMaMnnqyqhsuwBTM73XyFbTJHM78WMvc4AiWyc8L5Y+8r2Egubk3g6z6GXC5SLWaI9rXeuDncNEd98Smdfa7PbSHcf8fUlor1DteerG2wsTjNwrvUk0sTm8qGeOQdZfjRP77nWKoFOwux89TnZ3E7Q39te3kQhlya+vojLH8IbjLI+9wgEkVD3cz7u9bvQc+NYq1qmuavabOkagqQgNJVeqN6DU/OrjPZ3YqRVplfSdHv6mUxNcrMnyp3lNDcHAqxtF+iNVnO3ei/tXYN0Ok1nZyfxeHzX09h58zIA5Zk6ZdIteNHMbBzq6Os0PoN9tOmk07T6hv1pTUpLbU4cDCNPNjlPMZdCLZU4f+mvERm8QqmQ4N9/+K9Yy6/yZ259i+ELP8fco3/D9P1/hcc3RDY9x8Vb/0nN9kKd56hUModKpgF6+36W9bX/DbvTT7HYuNt83ePkeEKjp01bHpaf//mf52d+5meYmJhgYmKCv/23/zYej4ePPqqK3Xz44Yf8yq/8Cm+//TZDQ0P8Z//Zf8a1a9f47LPPmm7XMAy+9a1v8bf+1t9ipIGI1xeBQCBAZl8n3xeFOxAk3DdAuLefYFfPsSuDDtLbcuLt6d6o0b5eYmurLE4f7hNjZFW0WL0ywlo5/dNAsdnwBoIkthont5bL62hauuYzu91ONBoldUDDQhRteDwTqGq1vNO0TARBwO8OMbwvnNYbGWAzVTtr7wn18tq5t0ibOoZuHHLnQjXROrFWf7YvyeKhvkPNsDncaNphOfS1hSf0jV9ueTumYZBOJOjsr//cxldjzN+dItgbbaqzchrE4mlCIR8TI/2Mj/QTDrZXyZbJpOjuG6aSz7K19BTF6UVxe3E4m5eXnxg1D7b2SrLL2ysk7v+AuX//P6OXiyQe/JCtz/4A31Bzg7VjeJS5zz5mZWGJSMjP7dkEPREXL13s4lL4EsOhfnp8dmZiecq6gVM5PKcVBKGhUrYFmKbJ9k6JsxZLIYea/+76xgL6yjS2W0dXSemahnjCCWMwOEw83mbzwSOaOx7kYN5MPfSlLbJzH1Jenqd8exb9zhbOhJPY+7f559/+7/h3H/82X7v+s/ynX/m/8Duf/CssC8au/DnO3/wraGoKl7eD9cVPAMgkFpm6+68A8IdH2Nj5fD+XX/k/Mzr2N0ltT7d1Ll8kjj0CGIbBt7/9bQqFAq+/XtUfeOONN/jd3/1d1tbWsCyL73//+0xPT/OTP/mTTbf13/63/y3RaJS/9tf+Wsv7r1QqZLPZmr/nzYssa34RSU6tnIulq6dezGZZFoPj54h297A0M1XznakaCDYRUz09GXnTMBpez87ePhKxTQyjfm8PQRARBBHzQO8PURSxLAtVrederV4xdxNJ76HOYRZje910JUnGafeiSA5MQyfYcVi+3zSNprPJQKST5ObRKp/PsDsclIt74ant9SWiPYNtJeRlkltEeg673E3T5PYfvIdskxi5cQ5v8PkaKwBbsTiDfd243U7crva9kF09Q2xvLiMIMHjuBqVCmmjnC9CqaFPAS8tnyMzexdk1zMjP/jW27/wxoUtv0PXaL+CMNj9etz8Aosjg2Ch/+O4sl3t9eOy1BkBXwIllmCTztZ4CTdV58NEKfp+fWCy227ZiPwKQTCYJBoNV/aKNOLaBxp5RM5vAXJnEdulLLZ17oVDA4TqZ3k4oNIauFUkk51pfqY33sSzKyC38pg5PF/6xL+EZuYYpuen++jt0f+VthMuD/IX/6Nf46Vt/Bq/Tj8PmZCO1wv/yvf+ZYrmaSzN88ZsMnftZcsk5Pvrj/ycOVwhNzfLo43/M9uodTOvwRDSWLXN7fgFR8WF3PP/n8XnQtsHy8OFDPB4PdrudX/3VX+V3fud3uHixWp3wD/7BP+DixYv09fVhs9n4qZ/6Kf7hP/yHvPFG40Zg77//Pr/5m7/JP/kn/6St4/g7f+fv4Pf7d//6+3/8RHAakUql8LchtX8SDMvCbPIwCrKt3clFU4rbyzhD1QHO7fWha40MhVozqZnRlEuW2V7OoTcwctR0GsXXOMF09OJl5p/Ub4RmWQa6nq+rChmJREgmk3UbyFmWxWsXGs8Y3Y4A2gFBqvce/x5BbxC5UZdfy0JsEr5zuqOoarLh9wcJdw2SilVj7aZhopaLON2tKyYDBKM9lLJ5Zh98tqtbUshmEEWR6EAPgY7T8Qa2gq4bbeck7UdTi6Q2FnB6g1Wdnzr9Xp4Ldh+kj86TKW0toucSZOfv0/naz+PqHES0uej+0jePCAPVMnrzZcYHuxhQNNx9PuTIYeOu12PSpy2S2koxN7nN4kKKuckEF17qYXk6SVdXF3Z7/SpFQRAOC1/uwywVUO9+D/X+DzC2lrG98tOHljFUlcL6HJZhoa7m0FPVcNFGLke398BE4Bgzqq7umySTMy0vb9H6fdXr6WXAV79fWA2ihFWqGiB9P/FT2Hc857Jix2F31pRnYwn89vy/5b//nd/gO5/89t6+xr4Cgs7C0/9A3rLIbq1jiDq9w4c7Y29sztGtLLIQzxGItB5u/iLR9lB07tw57t27x0cffcRf/+t/nV/5lV/hyZOqst4/+Af/gI8++ojf/d3f5fbt2/wP/8P/wN/4G3+D7373u3W3lcvl+Et/6S/xT/7JP6kVSWuBX//1XyeTyez+raw858S4HSzLOtFLsZXt67r+wvJkRl123vvkCGXZU3L2aKU8+cQ6ruDejMvr85NJ7slRiw4JSzMRlAO3ZhNvkKGbRAe8ZBP1Y+C2YBC9SVKoJEmEu7pZmT/cN8TtHsXrbRwiiUajJBK1ctp2ezflclXPwTQ1isVFCoV5isXFmuX0A/eR2xGkktygf+xW/fNwupDk53NfbK3M0DUwdqx1uwbH6B4eZWtljsWnd8gkN5i9dxub8+SJ4e1w+eIoH37yANMwyOTar+pIbCwT7j+HpNhYX1smGj0d7ZAj6XsJNh82XcQyDDLzD8ivzxK++lbriaxN8PidIIIgHx4GMvEt+q68zsbTeXoHA/R1exmeCKMoEpbUXB3a4XCwubl5yAOjr82h3v0++sIDlGtvY7v2Nsp4/dyd8JU3KSfWMNJllB4P1s5kRNV0XC00gz0S08Bub93LIDwHyTLlwmuoD39Y85lmaEgH9jX96ff46chr/NLwL+J0uPj2vX/G2uKnAHh8Pbz2zt/F1EGqJBi8/FPMri1RD9OycIZewmuv3y/qx4G2j9pms+0m3d66dYtPP/2Uv//3/z7/0//0P/Ebv/Eb/M7v/A4/+7M/C8DVq1e5d+8ef+/v/T3eeeewUuDc3ByLi4v8/M///O5nzx4EWZaZmppidLR+sqfdbm9o4T9PQqEQqVSKcPj5zBwTicRz23Y9REHgqtvB6tIKfYP9WJZFOp2hUijQ1ddb9R6kNlHj1SQtyzAxt9dw/sRfbOulqRaypJYe0Xmx1vXb0dfH3JPH+HdmF5LHhuQ5/EJqFiKzTItyQcPpqT+Yi6JYbTvfhFC0A0yTpZkpBsfP1XzXLHQmSRKKorC1tUVHRweCICBJDkTRQbG4iCAoOJ0DCIJIubKJrueQd9RxRcGioqm8//j36QoO4XN3UExt8uDjf8311//jQ/tyB47uzOwLdZHYWibc2cIMDwh2dBPfWMI0rRMZQ25vELd37/gKuU3Khecfpt2PIsu8eusKH376EF3XefNLN9sSe4z0jhJfnaVSLGIXweFvvyrOtEzE47gk+16G5U9goL4uUOLx+3Tc+knEOuWsxyXS30lscZ3O4VqV5GI+savxdP6t6zz547tc+omXkHeeg96BAOvLGfqG6tyPQlVk0e2u5v0ULVh4OE1l4z5D4xdw3PjqoVVMy2QqOUXEGalRpBVlG6JLwSxo8EwJ98CzWCxmsDvar15Jbj8hED539II7qPrzkOcXkQcvoS89RR6sdoBfmbvLwMj1muVKxRw3hl9mIupnauUBV/te4juTP+I/HdwT3Qx2jhJinK7BV1h78G8x9ArSgTy4Lq+T20sbvNTTusbTF40Tm42WZVGpVNA0DU3TDr0gJElqaI2fP3+ehw8fcu/evd2/X/iFX+CrX/0q9+7d+0KGeZ6n2q2xk2vxIhV1AVxeD/lslrnJGeanZtHKZZw+H7NPp5mfmsXvH0G5+ga2a29iv/kVlPHLGJk6oYcGeSDT7/426ZWnh4yVtmhisPg7XKglHae38cyrlXydUGcX/lCY+cn6vTga7t/vJxqNsjX5MeyERez2KC7XEE5n727GvsPeRaWyl+CryC4K5QweV4gOu49Om5dKOVvXWGkVlyeEVsmhVlqrVHC4vGytzB3bu9IIt7eLUv75NbFshCxLfPm164yPDrCdyBBPZTAahCYOYrPZME2Djt5BIj3tVwYlSglm07PES+1VYADgiUKlfkJ/dvExnu7BUzVWAFw+F+XC4RysxObmbvWYKEqMvD7B5LsPdr+3O2U0de+drqk5EnOfkF2bIqfveYrSqRKTmTLd40NMfOXniOsmi7NPmXlyD13by7HYKGwwFhwjqx42cEWnjKWbyMGqt+7gU5zLbiEeI3SXr2TwuVrz6icS00RCx5dUaIbUOYiZ2deDqVxAtu95Ji3LQkBAkGVC/iivX/46IgIziWn+94++vTu2dg28irVzcfqv/9IhYwVALywTFfNt5eMAVIrFU6tMPSlteVh+4zd+g5/+6Z+mv7+fXC7Ht7/9bX7wgx/wne98B5/Px1e+8hX+6//6v8bpdDI4OMgPf/hD/uW//Jf8j//j/7i7jb/yV/4Kvb29/J2/83dwOBxcvlzrbn/WXPDg53/asSyL7e1tOjtfkBv6AOevHK4u8Pvqu0wFmx1LrcD6PfD1QikJeqVa7eDpBHcEHH6o5NiauUvfpddxNel109HTw+bqCl19zQzUxgaHKAr46sTh92P3+SnG47iOCD0GwhEkSWbm8UPGm5Q7HyS38ohA9zCkFsEyq1UFsh38tbLzNlsHlco2dnuUka4Rbs9+wOXBl8lszmJ3BghGT14+3jVwifWFRzg9boLRo9so9Ix7Ma0SEqebiGd3eSgXMzhcLyYf6xmTM4sUCkWuXBjh4dQy05UKt66OY7MfHaJSy0UyyW38ofa9KwWtwERwgvn0PBFn+w1J8fUS++h3kX1RJMHCEiVkTwTR0nFEn39JOEBya51IT+096/L5CA+Emb83zcj16sBtWRa5jVlMrUwCGz1913DY7WTX9gbDxFaBm1/ZeYa2p+gL2CF6Dss0mZt+zNj56ndO2UlJLyHtNzz2DarPjBU4HJ3u7Jpga3uRpZWHRKPDuJp4WzLpRTLZVQRBIFs4uu3FM8r5LcKDz19uI5nZxLEvGXZla57N1Crj/efR8was57H1eOgNDfBrP/O38HuD/OGn/4afeuXPYlk65XyczaVP0OtU/z0zbIbOfbOtHlkAxWyaQOfRWjYvgrYMlq2tLf7yX/7LbGxs4Pf7uXr1Kt/5znf4xjeqyYXf/va3+fVf/3W+9a1vkUwmGRwc5G//7b/Nr/7qr+5uY3l5+YV7EE6b067g0XV911j5ItS6H4UQ7EH75H9HfvUnoJKD4FB1cAYopUAtQnYdHH7sfVfhiJizNxAktn48oaVW8U+cY+vTj480WKrHE0CURGYfP2LsUmuGs6lVcAQ6gX0GZ24TEnMQ3jNCFMVHoTCP3V4dEF8a+xKWZZEyBUId/cDpeBV7hi+TjCeZfHIPX2cPPeHGfacCgVskU+8TDjVOjj8Oke7LbC5+Svdwa+0PTotKReWl6xdJZvJEgh76esd5+GSG65ePniUPn7vG6urysQwWRVTIVDLY68xuW6LzInI8jXfgApX0NsWtBWSHB8/wCRsrNsHutFHMFHH5q5U3lXKeUOfhHkodw30U704Ri2/SEenC12GS00XCnReRKhorusWIUEHYyY3Ipku49od2RRl2Ku0EUUTapxwccoRYyi7R4dq7R3ML95Fch0NO9d69ndEhAJZWHjLYf2VXFPHgOJPJrjIwUL3H9z9lpmXyYGOegqZjWRaSIHKrbwxlJ5HZtAywLKxSFkvVEAPHMEabYVWNiZC/i9TGImtLjxE9PkxT5+XzbwGgx0tYO3o2A11jbG6vEAl2MtZ1npJewqW4GL74Mw13kUsusbLwe1y4/tcpl9r0fDYoYf88aMtg+c3f/M2m33d1dfHP/tk/a7rMD37wg6bf//N//s/bOaTPBVEUMQyjRo32uORyOVRVpaur6/O7KQShRoDqKES3F3puYilecARq13MGwQn4qhZ5gOo5xmIxIpFIQ2NVEIRTu6aVXI7M6irR8+drrmk9MaVGuL0+Onp7WZyeZGiihZhvvW17u6oG3PZ09aUUmQBRRJbdaFoWRfHtHJeAhU4ytkSo43gz6VSmzNOFFOODAaLBqrdpZl0jVXTR595qarA8r/uuut0Xf093doR59GSOcCSIZQlIbXQCjicTBIPtKw0DdHu62Sxs0utprXO2sbWEvjSNYHeAZbEd26bzy99AdvtQPAE8LShBn5Su0V6WHs0z6N/R0WkyFxu6cY57Cw8J2wQilW1mC1lEXz9Rm8JqWaWwvYinu3rMs4+WcPV4WX+0xUuXx8FQd8Olz9iv5TLoq73v9XKR4MXrh45BPHCv6qbJZCKFqmmkMmXk0qe4JAFRlA4ZN0830qxZUzunae2erIXA5Y5h/M6qoanqGrfXZjEsi9x6jBt6GjXzPtjdCLKENn0HeeQKUuT0vQ6j518jsbnIw3vv8vbXv7X7uWVZu4+S3x1kfr16HtvpDXq7jtYuc/t78QcvYHO6MYzDHpgfF348U4U/Z4LBIPF4nGj0ZFL1pVIJwzBeaJJtPeRIBH07jtLZevdn2/AQ6uoqlqrhONd85ur1evF4PE2v2cDYOMuzMwyfO1lCWG59nXwiQWhkhK2HD+m6ur+JXXueMV8giK5pLM9OMzB2xOy80aDvDFb/TAOScxAZx27vJF+YQRTtSFL1JdnRfY6Pvv/fMTj6HzG4r2FdqwT9Dpw2mbXNPG6nTLliEPLZcPhULg+2Hto6fV5807SujjCmBblsjomx6kB4lMmia9VcjnI2TnTs+I0Pu9ytqzHryzPYX9krfbd99ilym2Xlp8Ezo8E0qp3Fm3Fl4CLzk99j/NI3kB7/Ph02mfu5EoGdFguiKJKeX+fCxW4S5QrpWIYfPZwnL7mQbTLKchKnIjLo8lDMpXA3aH7aSEuhWCpzZ31Pa0hA4Fw4gMtuh57m177D6ODG4NGdwW2ywmsD1YTc2W2dztffOrDEVdQnH2NmYiijJ/d+yQOX0BYeoQxXvbnhriG+5I2y/v6P6Pnym6iVIhux98EQcGkRoj3X8Tp9TC894O7KZ3zp2k8cvQ/FhmWaZJPLdPa3+X75gnhX4BSSbv+PiCAIOBwO8vn6PWFawbIscrncbs7O54nocmGVW5eTBhBtNuzDwyi9PaiLi1TmF6jMNNY1eCa61ygBW1aUhsmRmblZ7KGjZ71qsUhuK0b3lSvY3W4Ul4vk06e7FUJHhfL0dPmQym4o2oE3EGT64YPmHbSPChPOfpftfafncY9TKi3tznZsDice7xh2h5/Y2lSDjTRHlASuX4hy92mcXEFlNbPK5Za7Pj+vl9Ln87Lr6QzvGisffHKfjmjzScHa3CM2Fp8i2xwv5AWtLzxBHqpWhmjFIpn5Odzdh0MxL4JipsTy4wWWHy8gis0ru6oeUBHUIpZsp1jO4i9uMOyyo6cSPP7gCcVsCUfAR3d3lJTbwch4H2GPzDfGenl7IESny8a07iaebSxyuL/RYmpqkthnn7D16ccEZ6e4Gglxs6eLmz1d3OjprBorRzC7nsDlaL8cWvb5qGQOXxPbxVcRHF7U+++2vc2DiMEoVr5WNdvmdhO5ep31D94nufWYvvNfJZm5i15RySTmGO29wD/76P/LBxsfsJFYPXIfsZVtsqk14MUXdZwmP75H/jnj9XpRVZVyubX+FweJxWKfu2flNJA8HmxDQ9hHhkGWmxoFoVCIWJ327c8IRiJsbxzOZSknE/hHmiej5hMJth4+oufG9d3PwmNjOHp62HryhPV79yhlmzfos/QdF/GBcwhGooxdvMTC0yfE1tcarNx8ZrqNjVXRQ3KfUJ7HM7Gr1wIwNP4TJLYfUCrsHefTp9/hg7v/jq0DWi+qqpNYzbP8OMHsnS10zdg1DV650kk6q9IRVJCk/2M7UeeX1rl0foTuzubPmiQK9I9fo3uw9VLXk2Bm4kjRaugo9qN3KczP4+ptLZR02pz/0kUGLg0zeGWEVjxinq7zbC59iOXtI5aJ0+HvZHL1EZm4xtSjBcLRDvTtIitllaia57P1TQIOG7OxPFObOYb8Lt4aCB0K7zzDzGfITE2z9enHbH7yMa6ODjpuvULny68y+lM/TfzRo7bPMep3Uaq03/RvcLyftYX6+XVy7wjy4AUqn9TXGTspNq+XyIWLZBankCSZy6/+52yt/4hEaol//aN/ynYxxkvhG3SHj/YaRXrDBKMjbbcY+KLxf+y32QkJhUKk02nK5XLLnhJVVUmlUoRCoVPJ1/giYRsYQJ2fr6qEKgq2A2XpoigSCAQaas2EO7uYefwQrz+Aw+Vie32NbDqF84DnxTTN3VlCYmqKSqmE2+ej/9XDyZ0uvx+X308yFiNTyrNw/zaSKGPtqNG6vD46hvcZQw3yHURJYuzyFbbX11iemWZgfC9EpFdKWA1KugEmp94jFB7lRqSb2WIZjyRi2zl+SXKhqklsthChzj4q5Qt0D1Yrtham3kd1GXzpwi8yt7KMbuhkcjkU3Y4Wl+gY9DFwKYxpmqw8TiI6qseuyCLXzkd4stJOct2LD928CCKhAFMzC9jtNirlCi+/VD+JWtP1qpfsRbXeMM3dHA45ECB95zNyE+fwDrSmn9Nwu5aFWdSR3O1r6giC0FJXYp+jxKQl8FLPnnEXlIYwtG3e7BeQ7DLZ1CLpzDZpn8rlrg5GXQ4W4wUa3Wf68jRmsupxEVxehn/mlxHq9HCSbDYEBPRKBbkNHS6/20nI5+bJ0iYXB1sP2wmCgN7E0BEDUZQLN6l88l3srxzWGmsZrf4+bMEgXd3VSr+SaMc19kvMz/xzcnk3r/e+w8+efwnTNI5spyGKIr7g6TWw/bz48Ta3vgAEAgFsNtuhRniNeNbp9EV3fD6SU6h8EiQJ++go9pERpGAIdfWwN8LhcOB0Osnl6ns7xi9dYWttlfmnT5BtNobPX6Syc2hqNsvKJ5+wce8+mw8esH7vHu7OTnquX8ffpGmmZVnEN9a58PobDF97iYEr1xi8cp2hqzco5XMUM2mgqrIriM37RUV7evGFQsw/3ZPyT899RmjitbrLZ4sZopEBOiLVgShW0fk0U9j14jgc3WhaCsuqusAFoapblElskpG2ONfzNgCj/QPE0nHKaoV0IcvFL/cQ6auWcIqiiCALWFmNjdh+hdfWf1NRsP1YJ+M1wud1cfXKBGMj/XT3dPLJ7Ud1vYC9o1dZmrxNJrldZyunj+38DbSHHwPgCAbxX7xM8s5t8stLLP+H3wXAKJfJz86SeHifQh3PYz30RBmrYmBWWtOeOYjFnpdxqlDG2LlWpmmysHGXya0P+O0Hn1Eq14ZOc/kK+XIJ7+gYlmmhlgvgcyB7eum1V0MxpmXVelWKOSqffY/KnR8iOJzYrr+F7fpbKBM36horz4heu0ri6dO2z22gI4jTprAer6N3Y1mHkoKfEeyKMPnxg7rfAYjeEMrEdSq3v9f2MT1DGryI+vC93X/nkmViS1kMfe+YtlUNyduNW1X46tBlvjp6g9lHv3Woz1kjtEr7GizAqYwNp8WZwXIKuFwuZFmmUGguBx6Px19Yj6DPG8njRrApVGZnMYu1LzeXy9U0lDY4PsHIhYsEI1FEUaTr/AWWPvyQ+Pw8/a+8Qu/NG3RdvUrP9es4WvBsLc9MM3ThQv19XblOemuDmU8/RPLYkENHa3UEwhE6eqoKvfEnP8Lff7HhzFwvpFA8e4nGXwp6OOd2sqnuCWe5XMMUi1U5bcsy0dQyj27/JhWxgEPZe3H3Rrq4fu4Cw6M9LO8MYNn4Nlvzs7i8CgMDPhKpvevaTvVPtZ3A6be3eBFNPI9CkSQsC/q6o3g9btQ6uVI2u53BC7fIJJ5vef0zBJcfS62+L/wT5zB0jb6f+EkK62vYvF6Sjx6SuH8fIRjA2z9IcXWF9e8dHXoQADnkwMi0H/4o5rexO6ulxEtllVGnnaVSdTsrMw9Ip2UmvNd5R/JSPJAHoRoVNjoEHBMTVESV7eI8udQcRnKaR5tbpIslgi4bPmd1ojaXSLG9nMR+62vYb34FqaP1cn5Rko7duX24O8xaPYNlewq26oeaooM9DF0ZZ+nx4dYdu8cUiCAPnke9d7ycFincjdTRjzZ9G9hpN9LvJfb0CY6dTu9iVUKO0Ut/jmjXJcKBDkYv/kVk2U52sXGoHWBj+RMCkVFcnta9S7ucJd3+6cPr9VIsFusmZj4ThfN6vZ9LO4FWsJollB4TpaMD+9gYeixGZbb2YQ8GgySTrTXqc4ZCDL7+Oj3Xrx/rOAzDwGZrfN17Ji60reTo8ftx6GlcXaMo3jA5NYdmHO6QKikODLU2oTlikykYezkvNeXWgoAk2rH5I5zrqnUxd4TCiKJIxB8kW6gmfBuaSnRwGFHM4Y+6ahz67RgLDkc3lcrpexe+CPoN9x5Mcf/hJJZlUa6o2Jt4NwXh6DYOp4Vy4WXUJ58A0Pv1byC5XHS+9iVCL7+Mq7ubjldfxR2OYAsEiL78KqKsYGiH77GDaLEikr/994xayuNwVquULAtkUUDfuYdsdheyZEfb2qL7pZ8kXHCwXdgrOtBUkZBrjFg6xeLiDBde+o9488Jf5OuXvsmt3m62iiXm0gmWsyk+XdvAIUv0nqAsuNm9rWnNE4fFemFfSQG58WTF4XJiqM2vvRTpQeoeRH34QdPlGq7fOYiZ2hG0s6CU11CUPIJQvV8FAUqmSUfnOfyhQfzhIdYWv8unP/w+H97+YZMtg1bJUiql8IXaCzl+ESYc+zkzWE6RSCTC9vb27o9sWRaJRIJ4PE4wGPzCGitAW91e28U2NIQg16ZLybL8XJtIPsPcyRU4bdRsAq9DxhWqVnZsF7dZyR32UMiWgV4nvuyRJGaLZYo7hovD0UuptEqoo5+Zh98h2HWBB8v/ilylfqjxWQ6PZUE5n8Plq3ruylsLPPjgLgDdwW5W40dXEFS3J7fVkbZ1Pv8XniRLXL00zg8/uEdvz56wn6EbxOLpmmWjvSNsrc69kOMS3T6symFPo83twRE+LE7W8eU3SN5t3qhUjjiR/HZEe/vPs2yzo+2opIZtMk/yJSK26nMrCDDY281qPI4gSgx4OljJxLAsiw+WHrOoLLJRnmJxZZqp9RSr8ws12z4XCXGjq1rd83JvN73+51O+XSqtoutZVLVx/pZUzztjGhxV0dbKnSx1DiKGutCeftrC0od5FgrzhhzomkFw6CrJR79PZv5jhpx2LnmqEytdKzP3+N8zdvmXsdns9A/XV7TeWvmMZOwplVIarZJvewKRS8Txhk5ZKO8EnBksp4ggCESjUba3t4nFYrshoGg0iix/sfObBacLI/v8mtUJThdGOl3zmSRJDVvQnxYby4v0Dg0duZx1RJXPfiqZBKWVh/gmXt39TG7Q/VQ0NXTx8Iy+y64w5nIQUzW2KhqSZMc0VewOD+VyHMshcKnvl7m3/L/W3e6z1443EkGrlHF4qvksL10KoqvPclE0xDr7fpGcprFoFrVjbc/v95JM5+iMBomEqgOlaZp8cvcxm1u1XiWH042mHq/y7zi0M37oaoVKC17J4xgrALLNib7Tg8onS1z0OAkp1fvaAnxuLxlJpDw5SXh8go35NL/9yW/h9/fxzQuvQWiIGxdu8Us/9w6LsSyf3d7L+ygWFygW52rzLU5wbzS6bJal43T2oWmHDf14tkmPp8BAtaVIHcqazuOttZpS62bIvSMILh/a9L2Wlt+PGOxC31xEUkR8YSeSw03w4k+ipjcAyC3fIzX1LvHbv8fq4p9w/+OnVNQKmUR9Ay0dn+bj976F29uDYm+/SaSha8in0R37lDgzWE4ZSZLo6OggHA7/WBgqz1A6O9C3n1/CodLZgbaxUfPZ/mqf54VaqeBwNU7ge8Yz2etWKK08xH/p7drP9NJuwuJ+YvkMUXvjcNOQ0856pepqliQHhlGmu/9lDFMn7Onncu/PsZmtDaf9YPkHzOfmUVWNdN5A8oTJZCrcf7rN09kMHl/Vtb2RitMT+nx6Uz3D7vJTzJ38vjJVA7Oko8fb0wsCGOrvpqLp2G0K03PLTM0tc//xLLeuXzykfqzpOsVM/SaEz4Ujqjv2Y3O6sIePp8LbCg5nEFWtPfdKucT849s43V7yyQSyqfOjZJLbk1O8fHkEuxFhauPf8YPFP2B7bgVJAGHjLm9fCGILhMmXnhnPAm73WE0Zfysk1wskN2pzA5t5TQVBplRaRlEOX6c/fvrHjb26igNch9fZzKV4b2GKkMtJ0V0kttZYa2o/8uA5UBS0hcdHL7z/+N1BrOReHpWl64jYcUSHSE//CFG0Y5V95PIOhi/8F1y88RKvfOWrDIxPsLV6+NraHD5s4jiC8OMxDh3Fn46z+ALy41iyLEhSWxL97aBtbGA74LZspTO1qsapVLZxOvuR5fZnCK3iCYZIx7YIdDQe4DOTHwAWtsjhOPCQfwjlgDcjlYnhdAWQj5CG98nVa2C3d1MsLtA1cJFcrDqTLqlZph/8LwQ6/yqOcxNohoZTcdLb28fd6ccsbCWIuocIeb3cuNQBGXVXa8Fswwh7XoS7zrM68x4u78lUoc2sihR2oG+3b7AAjA9XtSruP5pGkiRuXKmWpet61Vh8NDlfFS4UBAZdL24eZ+ntJcdqz9ELmkksYh7Iw7LZHbg8XgKRblKb61y+eYvejXU2y2XiqTg/9crXKWlpFnIxyrKOWMlAcBgK21wa6uLhwibXx3qxLINyeQOHo5q3kl1dw93VRXluDftIT8NQhSgLmEbtfWxqGpVUCtMwEA+8Z53OPnQ9hyzXNvLcSG9wo+8Gc7E5dKO10HyqWGB6O8Y7E1WZge4Lr/DoB/+GaM9YS6EVZfgS2vQ99JVp5P7q/WaaBtl8CqfDhd3mqj2vVAxj5Qm2a2/vfpaemSb+o3cJ3LpFaTWBacRIfOc7eC6fp6AW6fr6NwEoFQp8+O4f8ed/+ReZnNlg4KVXCSgyA+M/hVrJI0pi200Pv4iceVjO2EMQnltGuGUYWAfCP6089Lqex+u9QKXSWBWz+Y5bG7Sjg8PE5mfR1GalvRb+81/GVad3h12yI+6brScycQrFFB1HdNl9ki8hCwK6ae2qAQM8Xf0d7i7/Dh/N/mM8jt5qDxNDJa+mMSyD8cgYr16+zl/4+tfJ29J0hXdefv5e8HWzsLXEQPTzESI7iMvXSTZ5sgokyW9DjxUb6uS0im7Uzs67OqPcuT/JQG8H1y5PcO3SeMtev/j6Eun4Me9LQH3wPsrEzbbWeV4m6NLUHyFJNnpHahtgVvtc7ezbtFBLJZweL5dGx1GNCjbZht/ZQVQO47L7weGnlN4GBCRJolSOV/WOXMPIsgdRrBoLpfg21vQWgixSvDdD8eFc9b+PFmq9INbhJFnZbqfnjTdY/u5363paDhorAJvpTc73nidfzhPxuYilm1d0AtzbWOatkVohQWekq61QljJxHTOXxthYIJFcY2ntKaahcfvdf3oouVt78n6NsQIQvHCR4V/5q2Tv3cNUVdA0xv+rX2PoL/2naGmTxemqKvbIhYv4/R1k4glkdBY++RAAM6UiUNX9cbray0X5oiXcwpnBcsY+5M5O9K3W2663g2i3H3rQW3sgLAyjgii2V8VzHMZeeZ2lB3ebLNHaYGkaOvn0Gn3dzVVTn+RL5PWDs57qPkY7v0Eyv0DUd5kezxUQRO6v/Hu+N/n3+VLPlwDYyExye/UPiYTdhCK1ceZcqYDP9eL70tQj1DlOJr54om0IioQUcKBEjn8fTM8tM9jXSSjo4869p8wvrtHdGebmtfP4vO1578qFLMVChkzieAaLsb0OsozoaU/mwEilTn0gia0+oLP/JTyBxq0BTMMgndyklM3g8geoaCpiWaU8OYmRTpPNFHF5XGQKOu/dKZFz9KBqKi63wPLKJwiCiM1WFYvUyip6xsJ1Yxz7YDfuGxO4roziuj6OY7SHwnsPMdXq5MbukrG7DwcCZEXB2dFa7zPLsvb0ZbDojQZZiR2dC+SQDud/iQhte6BtF19Bj61RWptiuP8yoWA3I1d/gXQxXbOc4K0f7pPtDgS7g6E/+8touSy+iXOIDgceU8XcqRoTRZGf+wv/MUnVDjY3K4sLWJbF+tZTNtb+ENMy8UeObpK4n0IqiSfw/EKQx+HMYDljF9HhwKo8H/Ews1BA8tbOfFrxsDidA5TLqzid7XsLVFVFlltPOhVFkUBnN+vTDUSpWhgoLMtifuke/f1HNxzssCnIosB0oUx8R7JfFO0YRplLvT9FZ+ASXx77a3T2XkMK+CnrOX72yv9jd31BEFGNEufCA8yn9uLe85uLDHe2rmvxDFlyH1kSelw6+q+yvfbwRNs4bjLpM4JBP5VShd7uDm5cO0+h2H5yraZWsCyLzZVZ/MEIucTG0SsdwEhto69MY7v46tELHyDyta+Tm5pse72Gx6LrGHoeh6tx6wJvIMrawlPsLjeKy8nU8hzTq/NMuAI4zp9Hj8exTAuPS2FqNsU3fu46H99dYnZjnksDl5B9w6yu3t7dXno1Tu9XbiB5XYf2JTrtuN+4QunxPABOrw2np0HSp9Bc5PEZM5szjHRUB+uR6Aj/2yefcn10zzhTjfphuYM5aVtLT3EFjxfatF97AyGepFishjRt3i6WP/lDkrOfkpr5mOTsZ6SzOTbu32f1009Zv3uXjXv3yOzkpYx86y8BMPqtvwKA4nTiOXcexbFXii2KIueuXOXizVtohommadg8Goau1nhvW0VTKzXb/yJwZrCcsYtZqSA8JwXeg+7PUqmEo4WHQRAk3O7jSUont7cJtNmvKdI/iGx3kI4dnjmbTeT3dV1jfukuK2tPGey7XBNWyJU1YtnDg2PEJuOTJQaddgo7FQg2WwRVrVYz2CQ3oigi+f3IoRBuRzeP1v6weiymScjVy1J6kpDLyXZ+z8VdrJTwOg+7xY/C7Z6gUGgtqbBd7E4/WqV49ILPkWjITyyZYXktttOIs72YfnxtnvWFpyxN3sETiOCP9DBy7Q02llpvVmmmE+iz97HffLvNo6/iCoWp5E7PqFya/kOifdebLuPxB+kfq7Y0WEvE8Lo8XBk+jyhLWIaxK1nQE9QZ9OexTJNbF0MMBjqYL2sUFB9eV5SVe/cpZQsYR6jwiqJ4qrGvkCdEplBNJg64Awz3hinsk8L/3ccP+HBphvcXZ/je7BO26lzfVHyNSiFLZ+/4sY+j96t/lsXJaSxdZyubRfIMEBy+QXD8VUJjt+h44+fovnaNvpdfpufGDbqvX6eUTFKI71U3ifvfmYbZsLeZz+PBZrORTkwxOPpnONYF/QKGhM6Sbs/YpTI9jej2YOTzSJ7TS3AtT0+jdNUqLBaLRUItdGA+CYVMmq5jNJTrGBxm8cEdAh3VY7ZMk/Sj7+Meqt9KXtXKrKw8YmToZl13cSxXQQDqObB77TZuZwu78uWiqGBZVTfvswqWmdgHZEprGEaZVKVAWcvxg8f/AnV1lZuv/BKpcha3Up2trsbXjl0ZJMsuDPP5yfN/EUTkblw9x/pWgs/uPWWwv3XhspXZhzi9YQbP1brVXW4v8bXWEoG1qdtY5SL2l0/QcwaOlWeW2kyS3c7QMdTBxuwGuqoxeuscgiBRzpfxBGq9HVOpBQp6mcuhceKlBLlcBpdpQMZgqzRNT6QTpb+fyswM9pERrKUFDF0nMjBIOrZJWfQzs7JAZHicgCJRssJ4w2nUbJnO80d7/xznBsi//xD365cRGuUttXgdVhIr3Bi6sfvv1wfH+cHcU0IuD0Gnk5u9g4yE9zwnH6/MkVMLNUO8zelGaiI+2SqDVy9z/70PyISCOAtlSuUyLrcHTSuxsfEUSVLo7d3zznZdvUrs8WPUcplgXzVxvFRawWbrwDs6yvxv/UuCkSjnr9+o2Y975/0tCCI9w18htf18JiIvmjMPyxm7SH4/9pFhzGy2bh+gRqjl5jMmQVGQfIfzKb4IA1gj9jeByy0+xDf+Mjbf4aQ1vZRleW2S0ZFbDWPb4k7ionGgame1rLJYqjBCgWFH1bNlWcZuPyEwyVdSTK39B3S9iNPmRxQE/s3D/w/hggu5v5shtZensXVu9g4BkClmCTWIhbfG85xVfTF+757OMLeuXyAaDhy5rKHrzD/6mHD3EJHO+jkekqygNWhe9wz1wfuI/gi2a28e55BrEcRDCezNME2T1EaSwSvDrE2toThsjN4a5+l7j7DbRjHMIuuLj9hcecrmylNy2QSaaXA9fI7b20/JqQUezyyilTfp6O1hfPgqH9/7LoVStirFbxgosoxlWpTzeVy+ANvxIi6vTMXQ2KxodLhdSKIdf18YQTr6PpDcDpwvTVB6sthwmVh5m4p2PAP77dELeG0O/nD6aY2xAvBq/yhrmTQ3evYS5mMLT+gdu36sfe3HLUt0nxunmM4Q7ekltb3N3Q/+PfH4In191+v2Beq4dAkqFbZnZtG0NJLkolJZJzM/y/D4KD2DQzXL379zj8HzFykUCvjDExSyMUIdx+hC/gV8P595WM7YxbbTKVbp6UFPpdC2YiidzRPbsokSlmlhaCZOb/1Y8+dhmKwuzNPV134exzOexa+z2zG0QhrJedjgMrUyC1vzjA3W97zsp6wZSAdmiitllbSm89NCDhIZiJ5DVePYbBGWUrfpC16johVQFC99oZvEslNoRhmfPYhQESkLKma5xEhHJx8sTXG1sx+X/YsVc96PpDhRKwVs9qN1cb4oLE/fZfjiy00TLXuGLrAy94iB8at1v1cffYjUNdBWv5xm+M6fJ/P0CYEr9fd3kM3ZNfp2vBpjt/a6jF/+yt76/lDf7v//yeQD3hy9iCiKvN5Vne0ne2BoZGg31Pnq9Xe4P/UhkWA36ZzGheFRctkilWIWlz+AJJY41zvBk9UnjPZdoJxYO1QyfRSSw45VbmyQOLwuHq89xuPwMNE1UXeZ6Y1pRjvrh5SHw1H+oqd+YvpXRur3HjsNbG43ToeD4XPnia8/pm9wjGh3dX8HdYGeERwdpbCxweq9D3HYuqjoa8hWhFd+6mcP5eklYhtEBw1+mHZwwzLR1AwOV3vnU8ykd9Wzv0iceVjOqIscDGJpKkY+33Q507DwR12U8vVfRpZl1bXUnU4n289JqK6Yy1EpFXHX8eq0jkB2O0Zycx2tkCX95D1S979L6vF71aoD02B++SGjg9eONMiGI24u9Rx++F/1u+mwKVXdFFcYshsYRhFRdFJR8zgVLwFXFx57mKKawS67cdkCuBQPJYfBcDKEfXCQHl+IDo+Xd6fvM9xZX6L7i4AgWrQhKPy5k4ytEYj2HVkVIogiQgPPlL40iegLn5qxAmB3e9DKrevR6JqOzdl6OOPtics83ax97nOVyqHqpGvnXiczu04pEWc1v8qD6QxTGwLbC0sEjXlkUcChOClvzGCoBfwDl1s+hmc4LgxReni4VYJlWQhY3By6Sdgd5tP5T1lNHhZOK6klfHUmG89wt9AupSpUdzo37t0PP2bqg4/xd4TJJpcRRJlo76W9fak6c59Oktk8XMXk7u5m6NZP4B6W6bp0k77XXqtbVHDl6lXEUgdDXvtuGXm7VEpF7C0Ibr5ozgyWMxpi6+vDSKVQFxcbllKKokAuWcbpqZ+sa5XLCHUaC7pcLmw2G6raflfZ/Zimycann7Lygx/uNp5cnpth9GL7L8f9WKbJ9tICQ1eu0/XazxO4+AbBa+/gH7tOZvID/uTD32bDG2YuV+2nspBPMplpryT83swnXI99DLmNqiz4Tvfeqa0fkiqtMh17H4B4bgaPPUJJz+Oxh7CwcIQjOC9c2E2SHo/00hVqbwZbn+fnDdPKRWyOw5UhX1RyqW2C0dbyXJwuL7lsuuYzyzQxtlaQB+rP/l8EpUK5bSNREsVDc4yg04mgWajreYxs9ZlNza4y0DeEa+MjlKV1Kmzw6o1O7t5fp3v8EqSXCPmjTCeKeHuOEZKgGhqyLDDLte+JzaUndPRXtxn2hnl55GVERO4u3eX+8n3mt+ZPpfy7XMiyMv3ZqYSDANw+P8Gb1wkVBYSijHRACd2uigxdH6ecK5GtY7QIgoTPdwWHo3EJerSnl0I2S7QwSyA6zhclFHsanBksZzTF1t+P0tdH+fGTul1sfREnil1qHA6SZawGYmx+v59cLnfsYyul02x+/DHRa9fo/fKXWP/gAwxdx+E6+aAY7O7ZVUHdj2j3kPKGuXbxLV4J92FYIt/fmqVsaGyWc201dIw63Uh6EVKLYBpYRgVVL7Mc/5CB/z97/x0k2Z7edcKf49L7rMzy3rbva/p6Mxo/I4QQCM3CIvS+ywsrWGkJvREbsRBBwL68IFjMwkSsZlnBEi8LaFitNMxIaK407s71tr0r7116b495/8jqrs6uzKrMqqy+1ffmJ6Li3s5zzu+czDx5fs/vMd/H9yThxHXy5TQ+5ziRzDyGYZAoRAnY+ylrKlZ5dwW0nVpgwDt05Pd93JzkvKUHiWws4wn2HbzjDh29I8S3l6teK19/C9MTr7b60gAQZAWtAQmCrdk1Bs8173V7MN0qmc3hMJvQM2VMPQ70goqaL6GWigjhBObBcYIdQUrbd9B0g6+80omgFsDk5MrGAn6npanWFw9jOTNEYXql6rVcPonLWZ1T1uPr4YnBJ7gwcAGv08v1leuYlcMnyq4v3WTpxttIsgmztboI4TDG0PZ2CK/PS6lcxmNTCC/doJirfv5ZnVYkRaJzvJdC5nCqzgCCKJCObiFKIrJ8CO2iE1ghBO0cljYNIMgyljOnKU5PY5ma2rPdYq9fCi0oCkax2HLJ/+TKCqVEkp7nn7//Ws/zz7P4k59gH21OIKkWns5uPJ17V9e6ppFMRxnuq3wOU+4AU+5K0l6vzc3l+BpP+xtr4W4ABE9BMQWJFVRPJ8nUDE/2/yxOS5Dusf+WmfAbOMwdlLUCmWKEId8lBjynMCvVD9BYfp1TnbUTOguFDXT93gr1nsFQ+4FULsWOrcfT42Cs6JqGIEpkU1EGH0i6bAyhEqrYeZ+GbiAox9M4znP2LMkbN/E9ub9SrqQ0/4gPpQv4bbu/6a1kkg6nA3QRvaiBKBCZXiR4fpz8lbu4J5/j5nv/Nxcmn+PGnShPjvkhG+HdnM4z/WNYZRMzi7eYHD2c11PcaRnyIMXy/ho6XpsX76D3UOcDKGtlCvEoU899bc82rawT3chgsSu4GhQyNAyD2HaIU+fPsrq4TCa5Sd/4i5g6HajFHJnNWSyeHkz23dCxzeMgPL+JgYGqqrg7PNj9u+Gt0PwGhVSWgpHBZK9cx91r13CYFYbOX0C0uIltT+PvPrPneh5X2h6WNg0hCAKm4WGKCwtNH2saHaU4PY2ePVgOuxFC16+DrhM4Xy3OJskyI1/4AurMzL6eDjVewFDrb7+8vMzmQ52l73Fz9kNOjz9dc5tLsZDfR6tlD6JU6bvSVUnaVRQvna4RfM7TiKKJYnEdj6WbdCFMKreK3dJNh32AxdhV7m6/RSi9CEBRLVRVNd1D0wpkMjPIshubbWjnb3Dnb6jmn9//Cqq6t9NtKzihi7YqtHKZZGSNjt7mjd7Ozm4W5yqig1oyhuhoXgunUWTFhFajouRhat0XB5HKlfE4dj0TI52dLIQjyB4zerpEMh0lJ+mkr01jmRyiw9pBn/USNlcQLRulINgJWQIoooTb4sAkmwh6u1jfWt7nrPsjmk2UYxUtlY2VO3R3H2+YbeXWewyfe7HmtnS8QGDAiVpu3Jsa2tyiq2dnASSAoakkkpVnaTa8hMnhRy2msXh2F0mODjeB0W6Coz30TA6QDD30uzRg4Ilx7JqJockpnA47Qi5Nz8AA5rUPUBQXse0byHJzXqZ8OoXFeTJUsh+mbbC0aRjRbEb2+Zru6iwIApZTpygfUfZfU1XW330XZ/8A7qGh2tcoishWS10PgZ4rIygiarz2Cu0Pr9+gz+tjNbb7cPj4+sfcnvuIm7MfEuzoxdTkA6AeulYGUaz8WdywdQNLJosYX0ZJRdD1Ml3uSQTBjCJbcFqC5MtxpjpfYqrzJXRDZTr0NovRy4x3VKumFgqbFItbOBwTyHLjyXOK4kJVDx+m24/HwcOiFjKouobd6Wn6WLMrgKSXMHQddeZy032CmmYfCzC0tMXq7WX0QySLDnbYWYlWFhdLkSwz22nUnZCO5LcQXd9g5Nwp0rE4kqMSfjWUyj021GPj+t0Y17eWeLpvtzrH6+tAlhSWV+fYD1VVay42rKeHKK+GKYaiJBKbeAONh+sAyhsbFO7UUbCugSCIiHJt75TVoZBLlZqq+rW7XEQiEZKZDHarlb6nXkKVd55Bho6tow9H5/5GsqfLR2x977NX2Umo9vf20z86hpTZwjnyBPqdD5l88pcbv8gdCtkM1mM0to9C22Bp0xSSx4OW3r9yqC5HmLAKiQRbH35I16VLWL2euvvFblyjlKyvBKrnVUSbUjMiksxmGe3wE3Q58VitvDE7R/jyR+QLGtdkibPjl+jq2L/aY8IZ4E6ifn+ZSHSVfDHH7PwHdPgHyJVzFLVipbV9z0Xwj1b+uKd6G+Vi/8+jakVUNUWPezck1+Uax2vtIZxdRpLu6bjoZDLTSJIdm21o32t99JxsF0u2UOL6jet0Xji8sFvP8Ck23v0jpM7GwoJHQbLZ6nZvzqVy9J8eZPBs8/kriiSyHM+TylVyuLKFGJpWCW1F7i7hGelHK5cpayKRtQzFXJl8oQiGgazIKOYkF7v2nrcz0IPf38XtuWt1c0AikQiRB5RdH8R2YYy12btMTL7Q9HsySiXkri7UOmPf388wWLz9Lso+8gBmm4KhG7iDjefKrS0s4fF3MB+JMnlfKuLeZ9DYNGzzOinnqvOWYgtreAYqCbg3X/8B7o4Abo+dQjJM5xf/yuFCuyfYFdo2WNo0jWizHiq8owSDlDc2ql6zWq1kDxgrubJCcnmF3uef35NV/zDFRJKBn/vTdbdLHgvljQyCae+tvxFP0uvxADDWGUTXdAxdJ+j186XOUS7HDhbT67Q6CRVrvx/DMEhmYoTDy4yPPoPD7mEru8V6unpcXVPB0He8HSmcFh8mxcug9+KeMYPOYV4c+gYApVKMbG4eu30CRTl5Lt3jfA7+4fUbpPPN9wYCSOfyfHRj0KftEgAAksBJREFUlqXldZ556WewOjyHvxBRJk/gkVQGuSdPEfrwfRIze1sDiNLRHu1fP9PFne006ZLK7FaCJ4c6ubO+wTury3R0Btl46yNcp0/R0ecgmyiRKWfYXE+QV1XsNoWOOvomDpuDiYHT3Jy5gq7t9aQoirK/J66nA/kQWkOGYWAUCoj7KHjrmsbdD16jd+QC/RNP7Tuew2vZ00X6YUrFSt7Y5soqwf5eAgE/pVKJoqqSCM/h8FRUuLXyA201Iol9xxQkEb1cqYY0MChn8phcFcPJbHfgFJKY+84gHSHZ+CQKxt2jbbC0aRqlq4tyKNT0cYLNhv6QEJTNZiOXq99jJrG4iKFpdF5oTCRLtlr2feAJkoCpz4nsqX7oGYZBPJfFtVNhJIoifoeNrCARyue4nQpjamC1UiznCG5fZWWruqphafkms3Pv4nQGGOjbFXGSxOqGfol0lFt3forxUMXPc8N/CZetXomtTiY7C+g47OMnOPRyfBbLdibHzbV18nUq0mqRSGX4+OYc6xthnjo7xpnJ4SN/dktX7zDy7P6TXauQZBnZZCa3Vq0/opbUAyfTRnhmyEs6X+bZ4WFen5nBajJhkWQkScLn8iApCpqqIykibo8di8NFKJtlYnB/r45sUpgaPceduet7th1UZScLh2uAKQcCGJpW3YvnIeav/ZTxp7/UktJ7XdO4/v6HzN2ZJpVM4dvxCj8zNsrbd28zu34Ll6+PfHwbi6vSAiSxvEkxlSF0c47w7QVCN+fYvjFLZjt6f1z/YCdbs2uE5tZJb0dw9uwKe5osVnRdp7g1i2O0dp5dIxwm7+lR0TZY2hyOw8w9uo5Qo2qhnns4dOMGWqmEZ7hxt7ZjYIjotatNX9r0xgbnBqvd+Of6+gjpImmHwCudo5z1HKzJkYnOMjD+OUJry1xe30I3DD5YXkXQRfp8/QR91foJRa1YlWeQToY4febzfJiuFuqq59otFDYoFFax28Ywmfa2DjhJ2N2dpGqIex2VdCHPiM/LMyODfPPN99hMJLi6ssJ7s3PcWNt7vmg8ycc35whFEjx1doypsYGWGHnZZAaLw1Y39+E46Hz5FQRJYun3fpdiMlHpFn5ljt7JowvVCYLA8yN+IgWNL5+ewqooaIZOOZJA9rpwB6ykInlcHRZkWcbjsyFZGnswKLLCcN8Y6w8Z9k6nE8+Ol7MWstNCIdm8d1dyODD17Z/3IputyFJrvrtr737I+eeeYdXtZWBsN5dHFEU+d3oKx2Cl94+aS2DbCTOX0lm8I30Ez44ROD1C8OwYnefGQTNYf69i3EmSRM/pQYJjvbhcdmwdlaqijdlpMpEQeiENBi2tyDxJfDrfVZtjRziEy1mQpJpxAbvdvicstH31KvbOLvyTzQlOWYNB1HxzXYGnNzbJqSrOGqqXz156Aoej8RWXbOsgGbrNkNvG2aCfn8zMM9zhZ3D4NIXtpT37D7mGGHIP3f+3LoAkCDzltjOTK2I2d5PP7510VTVNJjuLLLuw2ao9A7FYrCk9mEeF2z9EKrrU0jENw+DK6jqvTowhCCKfGxrgh7fv0h8IEs0XMcsy0R215u1Egv98bYZEOsdTZ8eYGGkucfMgNueW6Z86ekl9s3S/+jmc4+PM/m//ivnLswxdHN53woptrLG9uFc9thaCICALIiZZxmo2kVTLlBY3KJgrWh8uj8TSm2+SicYwCzqa0bh4oc3uoFCs1hqxWCwo+3SM7/J2E04cLXm/FtliBovcmrYWpXwBT3cnSQOsNhtWa/W4qWISp8lBIV5JrL9HPpdm8+7MnvEcPR24+nvIRncLATJbEWwdu2XbVocTIzaPc+oFpBpCnc1gnOBcs7bB0uaRYqjantfsdjuFwm7+weaHH+IaGMAeDOzZtxGEJm7rTLHInVCYB9UglyJZrq0mDiUOpZUylMwOEqU0JkXhC5NjuOSKMVYMlwn/8AdoD8TuTZIJWays6rL5JCal8rCRBAGbJFLGBBiUShXVS8MwyGYXUNU0Dvs4slwdk49GoyiKQjQaPZIo33Hh8PQQ225N59h0vsDbcwtcGuhDFEVEUeSZkSF+8ekn8FstfO3sKTYSSfKFAh8tLpHXNHwdAQZ7D9fN+iTjP3+R4MtfZ+ypCUwHyM0LooismBq+v++Fl1xWK2NDfXxw/QaxhUVKyRTr168z9PLLjLz6Ciafh2g6WtNYbqaZ6n5IokSpibBfo4RX7hIcOt2SsWZv3WFweJDb2QLPefbmzGzm03RbXYiigvyA/L3V4mR95haFQmHPbzcbiWHzee7/OxdO4Oj23/93YnuLQF8/uZWbOIYuHvradU2r28/oJHByr6zNp5S9D8lsNovNZsMwDNbfew/f1BRW3+E7DjtHhknO7l2p1MJhNjPgdXOubzdUYwADPhuRTCVprhnDJZ/dBquXskmgkE5QKGwgCAKFwgbS4GkMt4/wH7+GUaPLbCi8THdw6P6/+y0m1oslrNZ+DEMll1sil1vEah24L81tGAYbGxtEo1FCoRB2ux2n00kgEEAQBKLR6J7zHIQk2VHVQ1aCHYAnMIJhaGwsvM/m4oc7//2AzcUPiIcaW/Xf4/bGJi+Nj2J9IAlTEASspt0JWxREkGSeHh5iyO/nUtDFR6HWG3LxrSiezk82JCea63smHqQi4ig0HAZ7MB3GkxfoH+vD6nYRWVlm4JlnEASBd6bfYXZjlqfHnt4TviytrSG5XZTX9xotgiiiNdF5GsBqsZPNtvj+LJWRGugr1AgWh71iQNf5eHV0JFGmnIth3slfAXAPdqLoJjKZDMWHVIwlk3z/+8rFk5hdDyvvaph3SpGPEg5KhrdxB/ZvePtJ0jZY2jxSRLsDNV4tgJTP5zGZTKy/9TadFy5idh5NA8Aa7KQYa1z87GJ/P2/e3q200A2DVKGM16ZQ1sp7EmP3Q9fLCIKA6AqQ3ZxB10uYTB1oWg6TzYUui5QLJkof/THGAw/qVCqMw+GvO67ZHMRmG8JuH0EUd+PsmUwGv9+P3+8nGAxieSCp0OFw4HK5CDWZIH2vnPowrBVKBxp4/q4pekaepXv40s5/n6F7+BkkWWFz6cOGz3XQfCuKIq9MjtP3QBm8VZEoHUO4LL4VpqPv8fDcOHx+LPb61TIP8+DnHBOKOLxOgqdP03NuV7jRaXFyuv80bluNDr+ahuR0ohf2VnH19QyyurHUzOXT2dNLJFxfOuCTZ0fpeOdnMB/OsBLdG6ZW9QKyshu+Mbud2N1eUpFKf7Kq39ED/5tZD+Me3DV0Ctl0RdZAN+CICbOtViRvNSf3ytqcbA5Zo6p0BtEeMlh8Ph+b775L9/PPIVtbE0duKsfGMMg84ILt9VixKhKyJPJBdJknvL0NDRNa+xB/8AKarqFYLFg7BsiHt8hm55BlF06vhdhiAmtvENMzX6f0wR9i7PQrisbXCfgq54lmitzZTJHIlXDJEsly/RVoqVTCvM/KUFEUPB4P202I9lVWcs1/v5vFEnZJZLlwuIaWLt8AVnuAZGTpwH2T2Rx++8nrJtssalkjup45Uq+dB6lVKvwwJosVm9uz7z4b4W3UHYP6Xk54rlzG1tVJ1/nzexKLfW4fW7HaRoRot1OYnkby7pXKN0mNKfY+iCRJ96+tZbQobaOWsS4KAiVNe2Cf+sd7u3oIzc0yf/mDmh6wUi6PbNpt9xBZXWb5+jX6pqYAHY4galkuFqrGPom0DZY2TaPn8wjWwyd2CVK1x0IURYIDA4gtXvk2GspZmpmm64EKAosiEXRVDKeAxcX1xEa9Q6uwObuJhq7jLqQZcY9gcngRDTtW6wBmcxBJEXFE5+l49jyCJGN69ucoffwDilszSO5doyhdUDnV7SKSKRIwKYT3MVgaSa41mUzIj6ByJa8ZeBWZ0hEmX09giGzqYONqenub0c7Dua4VUSBX3ptL9UmQiRXxddtJRg7f6O4eisdFOXn0cNfS+jq5Yp5rM3cx9N0UzFuhCGeC1WGvXG6JbHaeXk8vG3V+J3JHB5bJSeQ6Yd7D5EzIityQcdYI2Uwc5RCJqvFIlDu37zJ/d3bnb4bZ29M43E503SBXqixGDMNA2vFapIspHIqdcimNLO1N5ncP99DrHKQzuOtBSa+GsHdWPrvEwjre8UpVUWxjjdkP3mH8medJzb6PZLHjHGpM/qEWqUgYV8fJDQdB22BpcwjUUAg52Nob2zQwQHFpqWXjuYZHSM3vLwOejEWZvXEDl9eLWTHxvel5Cg8ZBxOuIGodoyAZzhNe2Z0gHO4++oc/j+LsYnXhR6xf+x7uvlOoBZXo7dusffABvj/z8/f3FyQJ87NfJ7IwjVWt/ikWyhpmuWLYuSSJO5tbVQaYYRhsbW3hdtdwwT+EpmmHKtttNunYq0jcyuRxykd7rBw0gd3d2qbrCGHDZzpdfBSqr4Z8GA5dFi1Uqm109ehL/HI6i+I8vIZIOBHj2sxdRBnG+oaYGhnhrRt3UG27FXy1yusNKr+PHm8PK5GVPdsP5BAfXU/vIKsrzfc1q0Vo+S7dI81P9JFwhFOnpxidGt/5m2DizBSd3V0sRDIsJvJomobVJGE3V37LK9kQQ65usutL2IJ71ZBliwlrwI1a3vVSZsJRLG5nJSGW3dwjXdcpFwospZexdAxQiKwhyo3lMT2MppYRpcNp3DxK2gZLm6aRfD4Kt24fPvNfFNFriMWJLUp6AzD7/ZTqNDC8RzS0zfi5c3R0dXOhO8hTDgv/6dYDuSy6TrJcYKtQO8HP0A2sToVSvtrIcdk76R/5AonAOOVymfCtWzjHxuh+6ikcgb2VT70v/By5ZIrr12+iGwaDfhvriTx93sqqL2hWiBaKxGIxIpEI0WiUaDRKMBjE1IALNxaL4a3hjt8Ps7mbYnGzqWO8isxpu4Vu8/G5lUvlEvlCkYHA4RNcpWOI0R+mogwqnc4ja2lcHS0Ihaoq4j4lwftxffYuhWKRc2OTDHRWvH12i42XL5ymoJeYjoTpqakSayAKFe9dl7eLUCLUdEn9YYTKFFk5arrGfQxNO5SE/X5GqiyK+C0K2bJOt9tK0Fn5fu8dIZoVtFJtZWbZZsF4oLFi54UJtq/PELo+g29i18i58aM/5qX/6i8z4hkBUUI4QlwrsbWJt6vn4B0/YR6dwlGbTw2S04ns9yH5fBQXFhFtVpSuroMP3MHU30/h7l0sU1PVGwQBwzAeoVJr9XksFguvTowSKal0mGSWCyV+fztOMqbxi3Vaw2iqjmyuvTIREAldu0bfs88e+J48ooHLaefGzDwWq4nJgeoT2sxm/P76Sbn1yOfzmM3mpj9TSTLvqVRohFZ8d8Y+DfuWQxFGu09ecuth37fFrmCxH87IqHERhzpsOxrF7/XQ21H7N/xycJjfffNHvOwOsF4u0/Pkk/ffryQ7MYxdg/3c0DmuL1/n4vDFxi/gGGU/iuUMkewShVKmSl9EQEBXVYS7azhGLzQ97tbaBr6O+r/HjXwJk27gNNeeYl09U0Te/WP8z315z71jC3hIf7CbKC9KEt1PniI2u4pkqtwrkdVlirlsxSuS2iR15Q+xDFxs+n0ApGMR7N7DV2U+StoGS5tDoXRXVF/NI8Oo8Tjl9XWU3saSUwHkYBAtlUJy7fYcEe12SvPzmAYHEQ65UmyKhyZGwzDoM8msFctsFIrczRZ50m1nLWvi8uYGT3ZXr0BsbhPFXG0ZdE3XKM0v0Pvy1xuazMqZDA53H6fdZlZKUW7duc2ZUxVdiHy5jOmQ7tp0Ok2wxeG74yS6OY0nWF/ZWAWUFtizHrNMJFeiw3aykwwbZWNugfWsQCGeo8/bXFiow+3hztJ8XYOlVMjxTMBJ19R5CtksoRs36DxfCaGYH1JXNstmHFYHK5EVBjpa3wBS13VKyTClXJxCboP5UIR7Fk8tu0eRrfjtg/R6ansYk+l30JPrwKma2+tdQyIeZ+rcmbr7ZCwCXwr6q377WbWM9YGKQ9Eus/6D9+n78nNVx4ZnFvEO7xU19O3krmhqmYXLH/Hl//a/R0smyU5PY+k/h+Q4nNFRLhRw+k62SvY92gZLmyMje72oVFq4Kz2NuRVln4/S8nKVwSL7/Ug+H6k//C+4fvbr+5bXqSUN2XT4mOvm6gqBh1ygHR0drK6uYjObcckyZqeVK+kcLw8N83u3bnC6owOLsjvBmSwyJkvtn9DHP/h9Lr78dcQaIZvtpRRmq4ync3diMVQVYUdHQ4yFMHf1sRGJ0NPRwd1IjPPB5h8osVgM3xH0bEDEMPRHKiRVzCfwd9dXNx7tDDK9ucW5/qOp1J7zO3hzI8ErjRoshRRY6jeUPGxIqBUszMzh6+rk0piTUCLPH7y1wnNPdRGwmsgVVWx1Vvn3mFldYnxgqO72tdnLDJ2pdEi22O3EdB29XK4bfhrrGmM1ssqN5RucGzxXc58HMQQDtVCkmAyhqbldv2etYjUBTC4/zp5JXL0PeWgPgXvyBZJ3327qmMWZeUYnxutuj5TKeE0K9oc+9+XUKpPuftRsksTcT3EMPMX7H36EI13E49wNh/c+cZq3/rf/Hw6fn+DoXuXkxSsfM/LUJURRxHC50OQSomDH3tucKjhUKoP260x90mgbLG1aguz1ouo65a2tpsJDDyMIAq6vfoXyygqmoaGa+2hlnWQ4jySLVZN+M+TSabr7964Ae3p6SKfTZLNZRl0uJh1+fu3tGb75/NO8uzrNi4MHr8TimTje8xeJ3LhFz7PPVG0rFVQcXjPFXHXei1YuIyKi6wYgMtbVxbWFxfuCelKTHhZd19F1/UjVQRZLL4XCOlbr0fvSNMpBxlE0k8HcgoqnpkI4sYVKLaqhg9Vz5HO3GkM38LgqSch+p5kyButbGSS/jdntDKIBF0d9KHVK/cvlMub9cqGM6mTb4NmzbFy+grO3B3cdr2qfvw+LZObHH36XgcGKh69esbxJlilkNrB4AsjmR3ev7dKcy043dJR98rSupnJ8sWNvMryua4iiTGzxXXxnvo4oy7jdHxPbSOOZrM7fO/P1zxNf26hpsASHRsnEKzpJgiAgWOz3/79ZMrEonscgd+UebYOlTcuQ/X7UcLhxo6XOqvSgcFAmUcDXYye+tX/PIAEBXdf3JNSl4nHsddrMS5KEx+PB4/GwvLzM4OAgX3Y5Gk7KMwyD2c1Znhl/hqS2Rnx2Du/42P3tikkilyztSTcQJRHBq7D43nsMX6p0Wr0wMszl2TloQuTrHvF4/IjeFRBFGcN4dOW/xXwa2VS/vHQtGiVZKHKm9xE/YA0D/KMQnj6RBsuDN5MkifzZlwbZiOdZjeV5dryDmbUkqm6g1LB5V7c36N5H2TS6sYC7q9qIkGWZvmcukVxbY/njH6J07/X+CYKA2Wzl5Ys/gyw7T3AH8cYop6KUM2FMwTH2SylOqSq2GoZhWSsiSQpaPoPiDDD9v/2vTP13/z31EnhMFgfrb95k5IVn92wr5rOkImF6JioepoqNf/jP93H6btpVQm1aihwIICgKxcXFA/fd142+z4/I5jaTjhYOzDFU3C5KqeT9f2uaxvLsLJsrS3QNDKJpOTStdmJpuVzG6XQyH87wlVOdO9e7//kAbq3e4vyOFoK7rw/RamHr44/J7KjNCqKAYpFw+qvdsJlojOXrV5l48UXM1l2vUbFUIl04XO+Uw1Q+7OXRhTqyqU3s7todsac3NsmVyi01VnodFpaTDTTKFASILYJjn2RfQUDXToa2C0CP18qFoUrehmrAzcV4zd9bKBojsE/CZTK6gT84WHObu68Pj8tKV/AsPT0Xq/66uy/g802gKK7HakKsR3blBrLNS+rmjxDKybr7fZjM8oJ3b8n9tYW3GcgXkG1OyukIwVc+x/Q3/wVaPo/JJFEoVXtcN356Hf9zo3vGAQgMDKE9UPZc+Yke8nf6mH03bYOlTcuR/X6U3l4K0zM1y5fvIcgyWnLvj19LJBDreECg4qVQzNKB4SBLIEAhvJttP3P9Kr1Dg0xeqLR2zxfWKRRW9xxnGAbb29u43B7Kmn5fD2XE18nd8P6l3KVyCYuya4y4+/roeuopcisrFPMVcTC724ysSMx85/8mtbjAxjtv0XXxCUaffqZKC2EpHMHlceOw7F/urapZ0pm7VZ1fW4UgKOj64ZRrm8Xu6iSX2quW+tHiEi6blYnuw4caazHqtrKcacAY9I2A1buvd8XT6Se+GW7dxTWBoe9vKJ3ud2O3yDWnNJulfv6CrqoHToOOwSfJrF45+CIfEwrbC6jFioyBVsxTjG9RSoaQ7C5Mnk58F7+C0eepfaymo9QJaYolE4KsELv9QzyTryBbs/T/+V9CCXTS1eMivLErnVBMZjEcAgi1P/1cMom3pxKKU7NJSvEtzIdIcDYM49CK5Z8UbYOlzbEgmkxYJidQw+E9vYPuYZTLiK69iYxqJIJ8QAmv1Wk6cOWmuNyoqV1hN4vVivxA0uw9/YiHiUajdHV1IUvifWMFoMflI5LdX3Cs3jUFnnqK6NWrVa+5unoQrVZ6XngJWzBIeStLeXtXpCuez3Gmtxf9gIdKqRTCYZ8kn981vlolXV7JY2lM6feomK1uSg9o3pRUlTenZ5nq7qLb43kk11CXA0JBJouJcqn1BuNBLM0v0NVAArIoi5RrqPtq+2imrE1/RP/UpX3HlUxWDO3Rv+9Wk129Q/LuW6i5JOnpD0jefZvs6g3yG3dJz3yAY+iJA8d4I57mJc/edhGLm9tMdp/GFhzFM/k5UnPvohbT2Ht62VpZ5+7KOrFoZeEWvb3E9rt3KAp51u7eqnmezflpuscmd677FiZfN5aO5nN/0tEwTv9eXaiTTDuHpc2xYhocRA2HKa2uYurf/VEZhgHC3o6xeqnUspJmURTve0rz2Qwma7VHpuI52D1/IpGgXC4jiuKhk1XNZjOlcgnTjmFkGAbF4hYWSzfeyUliM7P47lUYiCL2zl2vgSCLVT1l7i2whl1O7oSjnAo0rsPSKin+w/YVOiz3RMdimQwzWyFeHB9tUWirPq3Q/hElCU19tCGhjbUNJFHC4Ti4p9J4l4PbKynODXmqXtf2kbdXdRWT0joxx5OLTjkdxnP6lYb2rnenOCWRqKoRMFXfr/lkCvtU5TcvSjLeU5+/v+3lF85z842fEje8XHhqEF3TCL03jXDJwemXf6b2+QVx934VRDhknplWLp/43kEP0zZY2hw7ciBAeXsbLZlE2pGSL6+uYhrY68YUTabWuil3fteh9XV6R6pjwjbbEA86GVVVJbCjRHt3K4WqGTgeKk20m2TShTJOS22jqlQqVVX05PNLyLKbYimC1ddBamVXulyx2ylnMph2ZOYrRtzeMTucDrayGRaX7jA8tLdKyWQKkslOY7eN7Zwz37Ta6P48uji3v3uSKzffQvEM8dzY3gqJVjPusXEnluG0/2gdwiX50RgsywtLlItFBEHE1+HD29dYTo8kiswmZyjcsOJy2cnnC9gdNqQ6lUPZ6DZmd2NJ2weFpE4+Iq2o3H/e6+RHkSRf2KkQMgyDW3dmGNznO/KfPc/TgSB3X78BgK3Ti2EWGDp3gY4apebr07fxdO3meT3a5cQnTzsk1OaRoHR2ooZCu4l/ooRRL0mxRYlguq7fP5+mqXu8DqJoRhQrhkcqlUIURTJFlXxJw2M14bWbyBSrQytnu4a4FVqqe05JlJDEB8sxBEwmH2o5AVTat98zJvKRMPIDHYclu4LkqKx4liIRbDurn/VYjPT8VQI2O6nlG3vOKct2nI4pRLHy/nK53H3DqxWIkgVNO3pzvkaw2n0EBkexux5NX5Nuu5lI4ejhM0mWMVrcvLMWaqnM2KlJRqfG8e6jtFqLzu4Al86dw213cXHqNEGPn7NjEzX33V6fpXfgdEPjfpIaNK3ANnDmSIskXdd5Y+XHhPMxTA94A2/PzDE2OoTTtb8xnLgzy8CpAXRVY/l3P0Aas9UM1cU21ykXi/i6d0vJjXwCzM213XicaRssbR4ZptFRSguVhmWmvl7Kh+1F1CDp+Tkcw7ur9EwmQyQSIZFIEA6HKZUqyaTJZBJRFPH5fCyEM8yHM3S5LfjtJn50J0Thgdi/IiqU90luffjhLYom0pm7mEyV0tGO06cJ7eSyKPbqcmnRplCSBa6vbqKqGhPdXdxeWydXKvP0ky9iZGJIdm9No+U4sZi7KBYP7qDcKvqc3ZhFg/nk8d4frUSgsc7ZR6Fybx1+Yr3Xs6ero1KG7HbUnkjrefrqYfUPk9uaPnjHE4pic+M+9XLD+3stfpZTa/f/PRuf5fmel9nOrONTJC4ns0QjUdwOB5YG+qN1vfQ8scu3Wf3BFZZfKaN1m+gc2utdTG5vMXR+N5fG0HUK2ws4+msbnftRzGX3hMgfB9oGS5tHhiCKiA4nWiKBlskgOZvXF2mGUjKB9X7yrkA+n6ejowOHw4HL5SKdThOJRFBVFcdOVZLLonC2t+LStSgSf/OL41geErDwWV1spGI1z+m2u1nc3i3ptlh6cNgr5Z0AssWCxe0mvbWFoe4aPh8vLJEtFVkOp1iMpBntDPL+3DwdLhfjXZ3IFgd6KYe9ow/J7iG1dqdVH1ODPNpVdI+jB5ssM5vYW8XVakSBHcG+w1PO5pDsB+eSHIXotTk03+H7KOk1vkMtk9lTqReau4F/pDHvCoClo59yOnLo63rcCNj85MqV5HvDMNAMDUVWGHSPYNcjeBSZjzfD9PXWLtF/GEmWEaUC2VQa6YNurGrtvBLhoRYgiTtvYeocRzpEvloumcBep13BSaZtsLR5pCidQdRYjNL8PHILwxY1eXASEoT73gxZljHvNBPs6Ohouqngmc4+ZiK1V/+DgUGyxSwf3PiAGzducOvWrT2ToWd0lPz2NvlkEl3TWNoOYbGY+XBhmdlQiEGfl7dm5rgwNEBwx51czmdRrJUJ0d7Rj6GVKWVqV1/B4++mB+i2d+I2WZmNLx3rec51OLgSSh+84z6U0mlMLTDADcMgenOeyMd3iXx8l+iNRbSdzr1La3H6alShNEotp4kaCqNGq43vbCGF296k6OBjpudxVDrtPWxkNplLLDDqrnhDnCY7RTWPurnJC4ON91XTVRVrpx/N4sfbmefs575YZ8+HChRySUTL4+clOQrtpNs2jxzzyAhGqb62x4O5AIamIRyy8d+933c2k6ZUKuF275XLPiyDniDz0W1G/XtXvJqh8cy5iiS/ruvMz88D0NXVhXMnwTZ44QIb2Ryv/+hHTF26xJA3yC1dYz2ZIBeK8kuXqvuk5GNbOL2753IPnic++wGm8WrpfwCbzUY+n8dma93DTJZdlMup+56iR0XQVglfzCdXGXUfj2y726SQ1TIH77gP5VwB2dN8N+0Hya6Hya5s4z0/gmyzksyWSMYzFC7fIZouoXcGsJn2PrLX4jl6PdYDK51qmbCCKFTlkqmZNEIL75tPKz6Lh+uhFWRRwbyju6RmkmyvrXG69yKOA/JWHiS3vYVit2Ny6VCoH0J6+PuVLEdLFH8caRssbT4RhH3K6ZSuLgrT04gWC1oyidzZhdLZXMfh3Poa5mBlgt9aWcHf3XNgqa+uGwdqntxj2NfJTxZu1DVY7iGKIuPj4+i6xtLSMplMhu6dTtf2nm7s2yZ6vBXX7Jm+PrKlMk8M1lAWzccRu6tfNwQRrVxEeqj01Gq1Eo1GW2qwmEx+crnFR26wQMVoKaTX2cxG6LafzK6yqmFgkQ+fKFzO5cmuhwk8d4brS3HCySRBrwWH1UQ82MPgGRtex97JbDtVwGlWWI7mGOpo3vuil0rwwAJhbfE6A2eeq7nv/uXfny0PC8CQexi7YkPXNJIzbyCabbx8/vNNl8hvv/0Wlgvn+fE/+4c4vH5M5iKTz++fU6OX8hjZMOLgXun+g9B17ZE2NG0lj+dVt/lUI1qtWCYnMQ0OYj1/Hj2bPfigh8isr+EaHLqfqDg9XZ0UmC9pfLxcCamoms7sdprp7TSD/sYf+uc6B3l/ZbbqNcMwkITqiatQ2CCbm6W/v4N8Ps/c3ByRSKQiuz8/d3+/aDZLwGar3aROV0GqLqX2jT5FYuYd1PKjEu765CalAWcvoXztvKFWIAiV++CwOLuCpFcPnyScuLlA4NIp7q4lGet18cWL3Zwf9DISdHJx2FvTWAHIlTTcNoVyA9de69uzTExgntzt8qsbxkNVbhVim1kiq/W9UIbWGqHCxwmX2Ul28WOSs2/gnngJ98izTRkrhmEQvvwxfV/8EonNDS5+5U/RMzlFocbzTtd11NKuKnP85k8xLB6cXc17HVOhEK5gcwvAk0LbYGlz4tFTSYo7YZVGuffgWFtaoGdwiP4HROs03WA5lmWqy8nMdpqlaI6xoINT3S4kscYDJ3QXtveqTnbYXfhsdl5fuEF5x62eL+WrpPmhIlDndExRKoUYGRlBURTy+TwpUcTj9VDYUaVdjkQY7moiqVIQ8J96hdTCR40fcyQ+2byYfkeQ5dTxqO4+GXByJXz4PBabx0Uxe7TS70i6CIKAvUbYpx4+m4nbG6m6ukCNcO+3kliZx9FbW+JdFAUkuf50YXym1EAgs3aT+N2fYO2Zwjv1M4hSc5//1huvs/3mG9g7uzB7PPi6gvh6+xi6+FRNocS5j96jkE7vGoaCiGSxH0rwsNIQ9tHIBrSadkiozYnHevEiWiqFGg43lKhbTCaQd0o2S/kCVrsDxwMNBGdDaSY7Kx1kJzobiANLCui1HwzjHT2M+Dp5f3UGEFje2uDpGmGiUimKrHgAGHwg5LMwO8vywjKTE6NV9oAazaMXNEy9O4mc9UJVogRiRQNEOGZFWEXxUypFMJk+mbCMz+JhPXM8/Xrsikz+CB4W4FCJp7quE37nOubJYZaieS4ON1e54bYpuKzygRNXIyXX0eQGowO1QxGGYewrrmbrmiKzdhNH39kDz/O4k926g6RYcUzVVqI9iNXv/D6eCxdxjlSSdbO5ONHtFc6++qeYef9tzrz6har9NU3FYrMz8cwLbP7xt1BcHdgGzlPOHJ/H8aTS9rC0eSyQXK59Gyk+SGpmGs9URRH2XimgtuMBCaeLdDotza1MDJ39ntaSKPHC4CleGJzilHOQoL+H2RvXSMUqDxSrdRDdKGOuMdEPf/WrxM1mouk0Lqv1gXOCaJXRS9ruC3Vw9p2qWebc6kohRXGhqkerpjkqNsVCptTYffCoEZr8vKPX5ohdnsb39Ck+3Mg1bazcP28D9/JyLkaf3VN3u6GqFeO3Dg6vBaujft6Z2R1EzdavWvu0oKlFSvEtrJ21OykfRPT6NbyXLt03VgAKMYONcMWgnHj2xaoGqAAf/5fv0j1RCdt5nvxTmDvHMAcGEaTmZfXzmTSWfRrLnnTaHpY2jw9NTAj3HuKCIJDJZLDvaGRIooDY7ErYHoAG5MdXN0JYLSbcXi9ur5fN5WUS029jD1aa02XZBKC0sY3g6QXDQCuVkMsqP97e5s8/89T9sQzDAH2nv1A+jmF2V217cJJSrA60hyZxp9NJOp3GVaO55OPMqLuf27F5TvsON2EcRMVdfsh1XBP3VXYzimw34x4b49pynFfPHG9OQapcxKNY627fmLlC1/iFutsVc6VD+n4IkoSulhHl1vQCO4mkZt7EPfX5utvVkoam6phttT8DvVTE0bcbns6Ek1x57fd48Rd/seb+mlqmc2gExXSvEimGpWOAzOJVXGNPN339hXQKb3fjJdcnjbbB0uaxwdinA3FxYRFBEhEslYfy6vwchUIexWQmn8/fl6o3yyLZokq6AJFMiR6Ppaojc00O6NQLkM3miaeynJ8avv9a9+Ag8ewG3tHqTq+l9BuYzp27/+9ewP/T16v2kVwm9IKGIApkNuex9+y62m/HbmOVrIx4dldpD6+yTSYT6fTxeENa0SzwJHIh4OByOM3TnYcrf2/Gv5Jb3SLwzBluLifw2U2YHroHP0hmKGgGHYrEaefRq70ueHt5O7TIoKO2vkpBK2A3HU34zjn8DMm5d/BONtZE8HEjtzmNpWNkX4M2HSsAVBkshXiM6//L/5fuF76IJFVPuaG1OYq5OG99+9/xlV/9m1XbPvqD38fm8TL1/MtkE3E0zUJycYH+ofOwoSNIn73pux0SavNYoEYidfNXtHQa0WHHNDiIaDEjdQRAEPB2duMJBO97VwDsZpnlWI58WWPIb2Mtnmcx0nwV0sPMLG1UGSv3MIwauQOihPFQZY/dXj0piWYZ2V2pDNFKJRSLZWc8A6eyN+9GdvdQjK7seb3VmM1dFEuPTqa/FsIxVSu5TQqp8uEa+ZWKpaY8d4Iocnczhcdhon+nHDmULNzfHimqvOCxEy5r/OftvbkK4WJzlWFFrcwHsfWauSyFRBTZeXSNIlGSj+27+aTRNZViYh1rcP+GnIIo7HEEL/7b/4PJ/8d/R8/nv0jPl75ctW3kiae4+JU/xcRzLwKV3/cPfvt/5ebrP2JzboaRJ55GlGXsHi+5ZAnF6at48g6xYHgUva6Om7bB0uaxQM/nEay1XdpaNIqyU6Ynud34hkfoHxmlo6MDj8ezR4/k0pCP4E4ey2jAQbfbwkL4aMJhmq7z43ev73ndGhwgu15d+iyPnEOdv1b1mmNwmLn//Hs1xxZEBUOtCO0JgoBqqHs8HA5/N7nE8SSkPogkWdG1wsE7HiPHWZHSqA7Pw6zcnGHw3HhD+2Y3o1i6fKglnUSqkgz+zu0Q6/E8781GuLwYpyOp8looiQB0mRR+HE0yly2g6jpvxNL8IJYiXio33L/ojdAif3PihZregY3VOwwMnqtxVPNIThdq8tOXy5KcfRP3xMGeI03V0dTq78T9xJO4R0aR6mhP9U6eRhQlCtk0f/ytf8mZV7/A6Zc/x/gzL2B5oN+T05bC0dmBoZYRhOarfJLhEK7A4Vs7nATaBkubxwJTfz/llRWK8/OUt7buv24YBnqxuM+RB2NRJBxmmbX44ZM5nz43jt+zN5nNGuinEK0uxRUdLoxidQmsLRDAEaz9MLEPnSWztGsMec1euuxdVfs82hDNJ1fCOhNfwWM6vqRBj1kh0aT3IpfJISumhnNfCtsRbD0BenxWLFaZP3x/lYkeF08MeXluvIMnh708OeojHsrxis/Jc14nHSaFkq7zh+EkBgZPu+y8n8zxv6811sPHIsl1r8+gdfePo/scme2bLRnrpJAPL2J2dSM2EILxddvx91SH1nJbB5fi67qOVlIZeeoSPRNTiJLE1IvVBpJsNlEKL5FZvIJ9qH6+Ud1zaNqh+g6dJNoGS5vHBvPYGObRUQSzmeLiIoZhUFpawjyyv5u2EYIuC06LwlwozWs3t1iKZJnf6dycLjQ2gRlUHgoPY3IHKCUPnlhMbjfJB4Tk7iEqFnR1t5WB1+LFKtdPoKy6pmPpKSQ88l5FmXKBy+E7dNnc9DiOb5X4ZIeD65HmvG0r12cYOt94x1xDrygg+5xmxrqcfPHJbjo81do9sigyZNtdkZd1A5ci02GSedXnYsJu4asBN1N2C//nxu69FS2p/L0bP2UlWx1GqheqiW0u4d5JCq+Fruts3bpFPl7ba6KpKldv3uXGnYowoyCIn4o+VvfQdZ1CeB5bz9TBO1Mx/MSHhB+9p86SXtk/XDvz/ttE11cYfbq+cm0ptoHJ34+ulpFMB3eB/jTSNljaPHbIXi+mvj5Ki4sIioKgtKYqwW1VGAs6+erZLoY67IwGHIwGHGSKKrPbabQDOvoO9wRYWN8blnEOnia9cvvA8/vOnKMYr55oisko2z/4V00LUwFMZ/NsFOr3bDosFksvhcLhVV2bZSG1wVp6nSc6pnCZj55rsR/NVgit3J6jp0bu0r48NKFblL2r3nxZxW/a/c6fcttZzRd5wV29ek9rGku5It/bjvOvVkL8QSjO5wJB5tPR6lPW8YolImt0dA7VvdSta9cITkyQWlu7/5quaczPL3JnZo47cwucPz3BcG8P04vLAKwaHm6Gb6JqR/N8ngRSs2/jGn/pSGMoLifsowSc2Npi4rmXUMvlPUm5D6KXcziGzh9KcFrXtD3l0o8jj7d/qM1nFkFRWuJZaYRutxXDZbAQySKLAgM+W00X+tJGmFNjFaVQQzdAMxCUygRo8XaSD69iDeyUNNZZhZo7Aqz94E8wT07g8Hgo3Pxjur78qwdeY257mczGEqbOCewOJ/F8Aa/VSvEYFruiqGAYx98OoKCVuR2dZ8gZxOfqOfbzPYiq6ci1WiQ8wPbyJrJiwuVr3IhKzq2huPevxjEMg9urSZ4crq7oed67N9n6Fa8TtyzzvMdOqKjikEXKqokP4pv8ycYdfGY7vbYOgjVW5OH4Jon1LaiTvqIXiwiSRDmXQ9gJJaSTKebXNpmaGMEs7wrWOVxOPMkU1+7O4TJ7GPD3cWX7Y57uvtTIx9IwhmEwnSvQbzFhP+YJuBBbR7Z7kR5Srm6W/NYWnS+8WH97OklwaITN2em6+wAUN+dg8oVDXUMq/PjK8T9I22Bp06YB7iXollSdhZ2qomG/HVGshEeu3VnAMMC0s1pWwzkMHUzdlcnJ3jdJ9NrruwYLtcuD3UPDuPr6KaysUE5H8Q5NchDJO2+iuLvof/nPsXDnNiOnTjMbjfFMXw/zuU82QfawLKe2SJdSPBGYfOQl1JeCLj7cTvF8j6fuPuG1bYqxOIHebhLTK6jZfNXKVxAqoR/Y1Rw0NANL0IOjv2vvgDu89f2r6KJE8PxgQ+/brci84qsYMr3WnRCS4sYUX+OLPWdIlQq8FV7kqz1776NwdB1rxwDrly/T++ST91/PJhKklpYpFwv0XbpEIR6nWCpxa3oWk6Jw8Uzte7Kzv5cHg3XH4Q1bKZQYtVpYLZQYsR2vwZLfvov31BcO3nEfIlevYHLvr4VkttspF/LoarmuJyQfXkF0+Miu3sbWe6rp6zCMx1eO/0HaBkubNk1gkkVGAw503WAhnCG2sYYgSZydGMJq2c05ECQRhGr3hrN/kszqDI7+CaRgN9rWGnL33uZlgixDNodr+AyE6oeSSqkI6bvv4DrzORR75aF4L39A2pnsrKJIVtWwH6GTcC0EQULXVUSxtY+QsqZxIzpLn8PPoKvxvJBWYlUkSvvkYWxHckTWM3RbTWiFIraeDmS75fCCczskYxm8ASe9w0HupBPouuPQY17qGOGt7VkEwCoKXItv8ISvOlfFMMDqdOF0ukitr+Pq7SWxtkY5kaD74m5SZ8lmZ8vr58m+HqQmrkcUZcpaEUVqXb6FboAsQPmY82SSc+/gHHrmyOPouTwdL+zvFYlvbtAzOYXN7SWfSWF371U8zq/dwf/k10jNvo994MyRr+txpW2wtGlzCERRYKzTSaRopWNgaM92o0a+i8nXTXa7EueXuscoXXsTahgsAKLdXlmm2zoqzRd1FSwu8FRCTuVkiPzGLP5n/nTVcS6vl/nVVRyWSil3j8XEbLbAeIsNFoulj3x+FZtt8OCdG2QtEyJaiHOxY/zErgYjsTzxeIEzz7ZeaTe8GePM05VxzznMvD63yOfGhg9ltLhNVl7q3C2zDuWT/HjzLiZRQqOSGD63+j6f7/08+VgMi9fLxtWr2NxuAmer+wFNR6I8O1A/MbceLpObRDFJwNa6UESfxcStTJ4eS/Oy9I1STIYQFSuytYE+YwcgNNDIcvjiU8x+8A4dfYPENtb3GCzRn/wXdDX/mRSKe5imfgnf+ta3OH/+PC6XC5fLxfPPP8/3v//9+9szmQy/9mu/Rl9fH1arlVOnTvGtb31r3zF/+7d/m5dffhmv14vX6+WLX/wiH3zwweHeTZs2jxh3VzfxGmWLss+C5NrnoXqAu1/u8FNcXARnJwSnQDLB4psAZFdukd2YxX1qb1w80N3DUiLJZMB//zWTKBy9sd+eyxeB1oyp6TqXw9OIgsGFjskTYaxMeGzcilZXC0XjeUKRHFPjtdViD4um6bz1R1fxBXdDKA6ziWg6w5/cmQGgUCweqfomaHXz+e4pXuoc59XOKV7tnIKsRGx1G90wSG9tEZiYwDNcnUA8E4nR5dy/jFyvk4zut3iI5lvboE8RBc46bfhqJCq3iuj8H4PRmjCkVjg48ViUJCaff5mZD96hc7iSlxeLxYiGQ2z9yb+inN7Ec+Hrh76GbCKOze059PEniaYMlr6+Pv7RP/pHfPTRR3z00Ud8/vOf5+d//ue5desWAL/xG7/Ba6+9xr//9/+eO3fu8Bu/8Rv8+q//Ot/97nfrjvn666/zF/7CX+AnP/kJ7777LgMDA3z5y19mff3RVSG0aXNYFJMZxWQml0xUvS7IIqKl+qFaiKzXVr6tgeRyIXk8FaMFwN4Bk19j68M/RrJ58NQwVu7hdVavDAetZlaPoVqoFWzlolyPznDBP0qP/eSIWnXbzUQeKGePJwtsbGc5PeHf56jm0XWd2x8v8NyXz+ILVOd8/PmnLlAol7m9FeJWOMZP5xa4G2pMd+UgDMNgfPQFkkENPVXEUFWUhwQWP1rfwiSKDHrq56LEsyWmt9NsJvN7tkmihM3kYCW12pJrfhTE4x/QcfZrWPyDJG69QeLm62RX71AIN6ciHbt5g+lv/i+YPI03tBx56hlMO55RwzDQEXCefhVr7zkQQC9mQGm+fUIpn8NsO1rbhZNCUwbLz/3cz/H1r3+diYkJJiYm+Af/4B/gcDh47733AHj33Xf5lV/5FT73uc8xNDTEX/trf40LFy7w0Ucf1R3zP/yH/8Df+Bt/g4sXLzI1NcVv//Zvo+s6P/rRj472ztq0eUQ4fH7KxSL5zP69e8zerqYaOMpeL3IgWBHKs/nA5sPcfw5Lx/7Ny4YCHSyEqsurLaJAocVeFlE0ox2ydFXXda5FZlG1Ik8EppBanAvTShKpIqvrac5N7e223SjF/F5tF03TuHtliamLQ8h1BL1enhjDMAye6utGEkRUVeX1u7M1920GHQgXUoTVKOlyjq4nqvtdbaQzpIpF+t37h0WS+TKnul1ki7XLdgecfai6SqqYPPI1HzelUgzQMZk6UJxePGdewXX6FRS3H10tkJp/v6Fx0kuL5FZWGP+1v4nvofDafnQO7VY9hktlwmWNtOwhu/4R2c3LZFdu4RxsfLxPI4fOEtM0jW9/+9tks1mef/55AF566SW+973vsb6+jmEY/OQnP2FmZoavfOUrDY+by+Uol8v4fPu7XYvFIqlUquqvTZtPCnewk3wqRblUfwIXJAlD15vq6SHabeiZDPnrO0q3DRg8PrudVL56xTtgNbNebK2XxWzuplg8WMXzYUK5OFej05z2DtLnfLTlys3QYVHYyBRYXE5y/nTtPlaNsLl8k42lK3tCOjPXV5g4P4CyT56D32blTHfF8/Ty2DBne7qwWcz84PY0hXJ9bY+DkAQBl8dJ7laZTI8d5SHZ+B6ng1eH+vl4c5uZaH2pfatJIp4toexTAj7iGWYtffI95onEx3i9z1W9JooiJlcQW/cEomChED/YW2Rye7AEAodOmM5qGjdzZVxmhcjaEuXEIs6xs5UKosdcqfaoNP2J3rhxA4fDgdls5ld/9Vf5zne+w+nTpwH45je/yenTp+nr68NkMvHVr36V3/qt3+KllxoX3vkf/8f/kd7eXr74xS/uu99v/uZv4na77//199dOXmzT5lHh6+klsbW57z6u3hEyW0uVfzRQtioIAuaxMSSfn9LaGpqqEl1fJbq2WlNVdz8MWqt8e5hy45vReXLlLE8GTqFIx5c42QrO+B3cDmew2ZqfJAzDYGv1FpvLN/F1DtM/fonVuY+Jh9bZWLrB4vRlXF478iFyMZ4ZGuDVyXHeWlohntsbilmL5xr6nscD3QQmh+igdpm1JIo809eD16Twzso66RotMDpdFvJljQHf/h2lJUFC0w9vYB03yeQVXK79vReOkQvklu+gH/A+zF7vkVJg7JLEzw710uf307W1je/5X0KyeQ7V8LBcKiIpJ/t31gxNGyyTk5NcvXqV9957j7/+1/86v/Irv8Lt25XSy29+85u89957fO973+Pjjz/mn/2zf8bf+Bt/gx/+8IcNjf0//8//M7/zO7/D7//+72Ox7C/W87f+1t8imUze/1tdfXzipG0+vXi7e4mtLRO5+iOK0b3eh1KxhJoMNT+wWsbU10dkZQl/bz++3j7im815N4YsZpbyn0wuS6yQ4HLoDqOuXobczVecfFKENjOMDXqaOqZcyrO2cAV/cIzuwbOYLXZk2UL/2FOYrGa6B88iS1YM4fDNKk2SiNdixvyQwbOVLOC0KCxHD+6LNWDvwG7rOdC4CTgdvDDQy+1wtOb2Ho91X+M1V1RJxbxsbswfeE2PGlXNoqppVDWDxdJ94P6e058jfvmPUAv7f76GejTjzLrjsdK9JdIz72Dso5S7H5loBKf/8KHMk0bT5r3JZGJsbAyAp59+mg8//JB/+S//Jf/iX/wL/vbf/tt85zvf4Wd/9mcBOH/+PFevXuWf/tN/eqDH5J/+03/KP/yH/5Af/vCHnD9//sDrMJvNmM2fzX4KbU4usqJg0ZNYJp4kvnCLzMYcit2FUS6hqWXsgV7sp547eKCH0NIZNFWle7zS00QQhH0lur0OO5Fkio4HRKtkUUBrceNCSXKgqmlkuX6uw934IrIg8WSwecGrT5rT3S4kufF1XblcYGv1Dv2jT+7ZJggCdmdl8ugfO8X6+uUjXduTfT1cW99ElhXOdlVCVoWyRpfbQjh9sGCgKIo4LS7CiQSNNBewKwqpfAGXtXHl17vrSVTV4NJ4kK2V1lYMtYJo9HU0vUBP959raH/RZML31M+Rmn0XNB3nZO0O2EKToZvSZva+yOQ9kqlrOJ94hfzMNVALIDXWP2zPtTxi4cXj5Mi9hAzDoFgsUi6XKZfLe748SZIObIH+T/7JP+Hv//2/z2uvvcbTTz991Etq0+YTxTZwHjEbwn/2JfznXsEaGMQ2cJpEXzchy+FKdkWrBXV5GYfZghrbefDvY3sM+v2sJRJ7Xg+aFLab7Ea8H2ZzgFKpduVKqpThcuguffYgYzv6MY8bzQilZZIxwhsz9I08cfDOgN0eJBZbPuylIQgCF/t66HM5uLJeCUV6bSZub6RwmBvrPfX0mJ9srrHV+9nOAHcijRkd0XSRj+ajdHmsnL3voWpt0vdRUdUCiuJv2Fi5hyAIuCdewDH2NKnbPyWzdPX+tnIqTOzK98nMXG4q/PqwsQJgt42gF+NINg+Zlds4Bj+7gnH3aMoM/Nt/+2/zta99jf7+ftLpNN/+9rd5/fXXee2113C5XLz66qv8D//D/4DVamVwcJCf/vSn/Lt/9+/45//8n98f4y//5b9Mb28vv/mbvwlUwkB/5+/8Hf7jf/yPDA0NsbW1BYDD4cDhOL428m3aHCv2AKS3wdmJ4qyUNsrlGGW9vrFQyJYpFzWcvr0rWPNYpVqktLCA6HQS21jHbG++VNElS0RKx59LMBNfAXSeDDbW5fYkomr6gWkDkY/u3M8tKBbu0PviLzQ8vsfTt+NlOZr4nsdmhVgczTBw2xTctuYaZUrmxo1or8XMh+ubjPu9eGqE7XVd58ZKEptZ5unR6hJwm9NPPLSKN3gy8g1TqY/xeOp3Rz4ISbHgOfszFBObJGfewz3xHLn1aXxPfI1c6XdZvn6VoQuNGa+1kGUnma0o7onnSM19gNRkk9dPU9fsezRlsGxvb/PLv/zLbG5u4na7OX/+PK+99hpf+tKXAPj2t7/N3/pbf4v/+r/+r4nFYgwODvIP/sE/4Fd/dbd528rKSpUX5rd+67colUr84i/+YtW5/u7f/bv8vb/3947w1tq0+QSx+SAyVxF+20E3dKQHRdEeeqAUMuWdl/f2GILKyk6QZZRgEGNtFYe3tQJmrSBTyjGTXGbU1YvbvH8PlZPO7Gaaie76oa7I5Wncp4ZR7JWJO/TGZXKbc9i6xxo+h8lkJ50O4XQeTQ1W04377Riaxd231/Atl1OoahqrtbqEfiLgxzAMZiIx5qNxAnYbAzs6LavRLKFEkXMDbkzKXiPI5e0ksn2XdCKF0/PJ3xu6rrWktYTZ042Wz5JZvw0CXHnrH+PpOMXw1OGNFa2YIz3zHmouhXDIJo/ZeAyHt7W6QZ80TX1b/+bf/Jt9t3d1dfFv/+2/3Xef119/verfS0tLzVxCmzaPF4ZxfwVula3IDz4gH55gBMCoyPoLUu3JR89VKkAEUaxr2Dw4Xq1magL1jaLDoCheyuU4K7kMRa3Ek4HHL1elFppm1Jx4AbSyCrp+31gBCL7yF4nf+FFTBksgMMnGxtWaBoum5VC1HGbT/kmTW+kMliMov9Zah5fLUQRBrtkvShCE+0rKlze26LI7uL6coNNr4anR/Y1of3CS0NrMJ26wZDLTWK2tC1PausdIz1/B5O1C21QZnvrTBx+0D4nrPyI//TpK8PD9tMrFAg7fp8tgOXIOS5s2berg7oXUrv5Ep70Tv7XyAKlVRSCKApqqI9bRtNCLRUSns+JpEQ5Ophvr7GR2a3vP670WhZUWKt8qiofL0WW8Zienfa3vsXMSiV2exvfk3q7F9oFzpBevNDWWYdQO0RUKW6jl/fWl7oajJLM5znYfXiW43l1UMVj2T94tZlXurKd4csRLv//gEKUgCKiHFBtsFYZhUCisY7cPtXRc5+gTWDvHKKtHF8lTXAG0+DqGYsEopEE+XMLtp43PtgpNmzbHiWIFtfbDuXTjHUynqhPMXR37P5RKS0tYJiuTpGwyUy4VUUz1K+UcZjNZdW/OjEkUW97tNug6hc/y6Kr2liJZBAEGG5gkD8N+hQLlVBbRVrs7s8kdJLd6G10tIcrV+heRkkpa1Ri2VX9OglD7MVzp12TU7Yr97uIKI34vna6jN+nbe24TZTV5YKlvtyEyNNS4/DxQHRZ9RBQK22QydykUVnG6LmCzHV8n8MGxn2/6mEImg8XhQMtkULe20EsFLGe/Rufn/jKZ+Q+xD1xs/YU+hrQ9LG3aHBeGAbm92hWlm+8hefwIlv3Fth5GtO4aNMVsBlluLgnvQQKK3NJqoU+Cknp8VSdz2xmGOmsbQ6nZZdyT9RNlXadfIXnnzb3HqRqyKFCqYQzVTpA0ULVsTWOlrGk4LKZ9jZVyDWO1UazWXpyOgxOmy4dI4H7UuaCZzAyx2JuUShFEyUomc4dCofW6XYZhMH3lP2K1Na+KXNrRdUn/yQ8oLM2SnX6dzp/5FRAENFVFNjUn/vZpTLiFtsHSps3xIQhgq44hl66+gdzVhzzcXIlieXsbYeehlQxt4QoEERooubUKAtkaEu5uRSalNqeUux9B06M1gAxA3kcO/qgUSzoOS22D0HtxksSN+iJohm4QfXuRjaUbaA8YDRtL1wD2eLcSiYWa4T2bbRiXs7b66mY6S+c+VWLziXlmEvMsJRYPlJU4DCtzm9y9skhnb/M5Eo3ct61CVdNkc/P09PwiJpMPsylAb88v4fM9f+gxdV2v+5lqWhlvZ/PeG1dHEDUSQbRa2FiYwz75uUNfH0AmFv3U5a9A22Bp0+aRoW0uUY5mUPPG/RVQI32FSmvrCIqC0lWRUNc1DZO12jtj6AZ6Ya9hMtnby8xG7XYBXebWabLYZYlci5sr7odFEbHWSYg9bkRJghpdt1ORPKWCSnZ+hb6/8BfpGjjD+tJVivkCuqYjluIkcwXsDyRBR6NzyHLjQmz36HU5WE/vbah4D1VTmfCMspFZ5Xr42gGjNZd8vbawjafDxdQTw7i8hwnJPTohs0TiQzqDX6ucVZARRTOqutvOYCW8zPTaLWKZGMlsjI3Y2oFjzm7c5u7a9T2vC4KAt2NvXlOjGKUSSm8vHR4Zz7nPHXocALVURDE3f1+ddNoGS5s2jwh1cxl5cIL85cvkdzqYZ99+++ADNRV5pxloKZ9DqhEKUiN5tGRxjytYlCSMGpMrgFOWiKvqY+k+7nZb6XJ/cg9kUY1Smtk1BEoFFUkWySVL2Ad7yC6tI4oi/aNPE966g6oWGe3oJ5ivbsuQzycZG2u8OeyDZAv1E2LHvePMJuZ4oe8VrCY3qnqw8m0j5DIFyiUVl+cIuUOP6H4rlqLIsueB06qIogVd3004z+aTjPWe5qN4kjvJNBvRZW4sfUQoUdEDm12/s8ebIghi3XJowzAO7dFSenqwnj+P0bObE2SoJTjBncwfNW2DpU2b40RSQNvxYug6os2G3NWNGo1RDoXQc3nSP/whejZbfwiPBzVSUZM1WW2UazShE0QB0SJjFPeGeWRq500AjNssLORbV7XxOBo/D7MUytLXsX9+kejtQLSYKN3+EIByQcNkldE1A8lixiiVIRdDKGXxB0dYuvs6JpMTXav2aJlMNtQ6idn7IYkijn1W0GuZdUxSJbnXqVh4Z+Md4tlqlVrNMFgLRyhkGut0r5ZV1ha3GZ7qPXjnTxjDMIhF38Tj2W2RoBsahqHdD79txbfxuzrYLpZ5uWcQn7uTJ0efZaL3LEW1zPTaLcpqkRtLH1PcCasWykUEhLo+In/3OaJbNw593Zt/8M8RlN1ctezKTewD5w493qeNtsHSps1x4uy5X9osdfUiqFmsFy+gdHWibmxgnhjH9txziPvkI0huN1oigbHTnVmUxD2GgaEb6HkVoYZq6VRngOka5c0AkiCgG60xNHotCmuPeSIvQDpfxmuvn+Ro6DoIIA+cQvL4Kd18F5vbRGI7h3Kv9YK4k3AdX8Jqc+HrvURRV9D06nJyr3eIWHzpUNdZqtMQbzW1hk22MeYdAaDH0cNp72k+/PiP0HWd33/9p7xz+zY/uHKVaCKOEW2sGefC3XXGzhxNpTYeWUUQjj+Ul0pdxe9/teo1UZCwWvtIpyvNepO5CEFPD36TTKhURgBEUcasWOjv6KevYwCTYuH88NPMbVxnM7bG7PpNxnqmUOt89mopRzbVXFPSB7H0nsIxfOH+v7VyEdnSXEnzp2HRUI+2wdKmzXEiyaBXDA25bwJta5H8xx+jl0rIgQCy34/kcFDe3KQwPVN3GNPoKKXFxcqQigm1VL0qlzusSF5LzeRNk9VKuVRfd2XI2pouziZRpKw/3g/Lu+tJ7Af0e0ovLeDsr1QJST0joGlQLhMYcN5vqyCU8xVXfscExBdxu93EYjHEh0qYFcWKrh3us5dTGh/ORvjR1c2qMESunKXjoWRvNb6KJ/g0qXyBgc4gIz09PDk+xoXxcQb6+sil0/uea2M5TLDXV7OUu1Hi4TUEBDr7j6+k+B7lchKTqbrcWlE8xOPv4fM9R/kBHSSzKGIVRYat1eXmdouTsZ4pBEHgzOCTmGQbI12Vf/cHhrizeot8qVBlICTC0wxNfe1Q15wPLVPYXqjqzFzfl1OfTCyK41PUoflB2gZLmzaPENPFV5DlPKahIQSzmdLqKqXlZVKv/TF6rn5YyCiV0HdCQa6OAOlodcNBQRQQ9+kJIyCg1Vl5KTtdnPVP8crsIHRN56O5KAGXhZHO/XVN9FIZ6YESc7l3GHVjsWofQ3GSW9+G7ZuAgKIoBAIByqXcka91bXGbxbvr2HV4ctRHt3Obm5Gb97drNYTodK2EJCu89vZbnBsbp8vjIeisvM/u/kE2V1fqnm9zJYKu6Xh8h9d7SYQ3KJdzeDr6Dj1GI8TjHxKNvonDMb5nm9v9xH3vzt21G4x375ZtB80Ksri/ceB3+bBbKp5Ql81Dj6+XldAst5avsrw9j2HoiNLhpQaswUHMgQGsXQ+ILx7iN6mWS/vqMz3OtLN52rR5hAgmC6LHj2gUKMyuo3R3o2ezOF59BfPISM1j1FgMLR7Hema3FLpZWf1xn5uZSJRTgdorrxGrmcV8kVHb0RJZRQF0w0B8jFraJ7JFZjfSPDHia6xU+qH3Vl68g+mpz1fvY+hs/PQOrrPjKAEPmlzgg7tLfOX5CxyVckm7n0eiqmV8iom82cZWNkSiEMNrqf6OE5FFHJ4+nvIO8tTYXiViQRAwDB1N05AeauNw+/ICfSNBXJ7DN6LNZ9Lk8zG6B2qXaLcSTcvR0fFq3e2dnT/LwtY8A4FBxCMK2LntHtx2DwAb0Q1+/JP/xEvPff1IY6LYqn7bxn4t2evxKV54tD0sbdocN4oVSrveE2XiIurSXeTOThAE5K4uJFelt4qhqmg77vni/DzFhQWMsop5dHeiMXQdvckSYofLTX6fxN5WGRh9ZhOrLZT9P26WQhk24wUujXccWtdFMJlraIvomEYmCX7uJdLzq0R++z9xWiwgy80JgD1MMr4butlaucv6wmWCfVMMu4cwDB3F3I/9IYMlm1rH5d2/I/Tw1CkW7tze87rNbjmSsVIulYiGph+JsVIqJVBM+/cyiqZiKJKA2966xqHTC2tEtnO8+uo3MNvcRxrLyMXJh5YAyG0vYQm0rt/Rp4G2wdKmzXHj6oGHEvGk3mHyH76DoWmIdjvljQ1Sf/InFG7eRIvtVHOIIuaREZTO6sZ4kdVlfL3Nu9ZLBxg5FlGkcEQtFVkU0B6TBd61pTiKLHGqr7lJRnho1atpBuGP7rL6Xyol6lqxRHYtjLyjE9PzpecRh/y4J2pPPhUJ/gpLMxvcvbJU99yby1GGJroxdAPQGJx49n5eidcaxCQrhEtlNK1MJhEmunkHT8fB/Z0kScbr9xPe2tXs0XX9MOv7KqJbs/QOP3XEUQ4mlbpJKPSH2G37v9dQco3+QG1P5mEp5HKcPz12ZCFDQ9fJb8ygJitJ0Gp8E7P/eENojxttg6VNm0eBoVe5auW+cUwBF4WbNzHKZaznz2PfqRYqr1eqikS7ndLqXglxu8dLOhpu+NTRTIYf3r5DwLm/dkaPWWHjU1Dl0wjvz0QY73bS62u+qZzxkDdq/U4Sz0QfntMj3PnH/4bZb/1fWLs6cIz0kri7RPzaNI5AJ+n49o6h8dB4O/dFKpHFZFYYGO9k+toymyvVeUq5TB6Xx44gCITWZwj0VE/ORV3HKook4stsLX2IpicRFQm7a/9+QPfo6O4hHg6h7VSjxcIJfB1H8xhA67qC70dZTeL3fw5Z3r8cXRIPn2NSi42tCF3B1iS4ltNRvJf+DM7xZyovHOJzy6WSWF1H/c5OLm2DpU2bR4FihUJ1F1fJ7cT+wgsUbt1Gy2Qob2xgGhnBspOrogSDGDU0V2xuD1qNbs+1SKXTzIejfPH0KUaDwX33bdXEIgmc6GqhhVCGyR4XNnPzKXyGrnPP7RDOhUkWk3S9eBpdLeIc7qb44hDin7mIKEvomoHisBJ47hw9r1zCFxxi5vIf7RlT0zSWZjZYmd2iZzCAzWFl8sIgmWQW9YG2Cla7heKOQalpOaSHFHLdisx2qYxdtODy9+H2jeHtaK4iZ/TUGRbu3AIgkyzg8jXX7+pB0okQFvvxVqtEFn5EJPITAKzWg70RrTadYrEkncHWhJcUp5/C9m7Lh8PkrxSzGSz2w4fwTjptg6VNm0eBZ2BPI0Rl4iLa6l2U3p5KaawgIEgSktuNUS5TWlmBIyYGXt/Y5NLQo42D91tMrBdPbh5LNq/icRwulySzsoSjr6JFki1niRfi2PoGSa9UPGFjzzyLv7Ob/GaE+JU72Ps67x9rkzzYxQ4yqQjJaEUXR9NUcvE8A2NdnL1U7TExWUyszm8ze3OFYqHE4vQ65p3+RqK01zOUL6c4bbcw2TVAOnE4LRBJlnF7fES2K9d3FCM2m47i8Xcd+viDyIXmKW0sYBaDDZX/aprWem9PC4dL3nkDBKmhdh31r+fxSXY/DG2DpU2bR8W9kFBiBSJziKUYYn4Tde56Ra9F0+6HgPI3b6JGo4h2G+WNGpNPA5UA15aWOTfQ39RD2qNIxGo0S2wGcUeM7qRyqMqLHbRiCXlH5E8QBHRDR5Rl1HwBNZ/HYXLgt/ophKLw0Hkkp4nAqfOszX+IWs6ztnCF9YWrDIyeralvMjjezfBUL8EeL6tz2wxP9tIzWOkE/PAEnSnGubHxQzZTc5QKqSNNzMG+PmLbW4c+/h6yIlLMtd5wzazdJD79BqIo0/Xcr5C98xE+3wsHHhdKbeN3dR64XzO08jY3UBC0IoIoHs1o+RTTNljatHmUJNfA5ICOMfCPorzw58ltzqGZFCynTyNarRSmZxDNZqwXL6Jnsxj7iL7VYuXmdWIb6wgWK25rczkaPkU+ssFyklmP5wm6WtODqKgVkXY8YF3PP0f8zq4WimQ2Yeupnhyz6TiX3/4WhVwKi8NJ38gTDIw/jdW+f86B2+dk7Gy14ZnLRNhaucX22l10XeXGzDtcGvgF0sUwofWrdA8+e6T3Njx1io3l5SON4QuOkIgdbYxaaPkk3slXsHQMIsomJJv34IOAdC6J+4hVPHtoocWSufUDZF8lrJVdvYWt71TrBv+U0DZY2rR5VMimSnmzrTrm7fiFv8bqjmdF7ujAPDGOXiighsMIUp2Q0D4raJPFwqLFyZZsJqPu7S20H8v5Iv2Wo5XeQkWMrngCV4mxZJEub/OJtvcop3b77kx4Jxh07ZYLK7bd3IGRv/R1Ol++WHWs3ell4tyfpVBYZ2Nxb7ffenz7p99lM1KpHLkyd5Orc7cZPvUihj2AyeIgtDYLUud9g0YxHZx3Ut7KokbzdbcrJhO93UcL54iigqEfQxK3UD1tyTZPY4fBkZR6a6HXkeg/DLbRZ0lf+QN0tYSWT6PsaLw0iqaqR9aWOem0DZY2bR4VngEI7G0/b7FYUB9IohUEAesTT6AEgxXDpEn3vtnhwGU28eUONw658QeYqhuohoG5BQ/1PrPCeuHkVRwdJRwEYO3spJxO1txWTqcppTL7Hu/vHOK5L/y/GT/3M3X3KZaK/PDyWwDcXppjpGsQs2Iinctgt9gplItcnr1JKBFhOhzi+7PXGO0eBiqhooPeo6EbCIqIoe5vULYi30M3VDaXbx15nP04FqOoAbZDURwHVN41g4BO7y/9fxBlE4dx3aSjEZwdn05J/nu0DZY2bU4AdrudaHQ3KffeZKHncgimvR4PSZJR64SKBASydaqINhJ5bqwlazZIW8wXGbG2RtL7UZSyNkssW8JtPVpZqzXYRXan7PxhRIsFUwsmsLur8wTdfnRdJ11Mc374FHObS8xtLDPRN8zZoQnmN5cY7Orl0sQFXpx4hqDXz93tt+mwN5BgLVSMFg6QogeqehQdhkDvFG5fD6H1+YN3PoDU4kckZt5EVKpDehZvP7mt2SOP3yyrGyFGh1qnk2JoZUJv/+7hjzf0toelTZs2x09vby/pdJpI5KEeQbKM5Pfv2d/ZESAZrt2BWdNU4qpOukY4qKTqTHQ5WIlV97TRDANJEE6kodEq1sM5BjuPVvIpWcyY3F5W/uQH5MOVMI1RLhG+chnnwMCRqzR0TWcptEp/oIe7q3OYJBMWk5mF0BLbscq94bDaSRQSuG0uJElion9XCM1n7z2wYkYQBARFQrLvb7x5urwktmJHej+KbMLm9CLLCul4Y12h62FoJTwTL+McfKLqdVvXBMX4Krp+UPizdff23dklSi2uhBMVM4rD86nutnxU2gZLmzYnAEEQcDgcaJpWtao1j40h1vCwCIJwXyE1n0kT31xne2GOQjYDhkHAJCPXmDx1wyCZK+OzV4+5mC8yZD167sqDWCWB3BGVc1vJUcNB97B1dzPw5S9RjMUoxFNEb1zDNdiPye058tg/vv4Ww8EBPE4XW4kwilTRihntGuarz+z2yPkrX/yLNYzLyr8beZ+yx4xo299gMXQDrYEcqMjKEslQbeP5Hr7OAWLh5aZbSui6Tuz2D4jf/SnCPo0F7b1niV793gE5Ja0zBMpllScutC4pVisVyS1cxn3u82S3lrAG92+lUJPPgKHTNljatDkh+P1+BEFge3ubcrlMoVDYd/9785Wh62zM3MXicLBw+UN0XSde1rDWkArvclsoqjpOy+7D/16X5lY3LOwyKWydEOXcXEnFJLf4cSdAIbRBx5OXMPsCLRmy0xPk/OgpBEGg29VJNB0H4NJEddPEWsmjimShWK7fL6pZEqEEgYH9y4ANXUc2W+qGJx+kf/QJttf29iuqh67rxG5+H8/kz+CdehXX8KW6+5pcQfxnv0Z6+eP6A7bw9u7pCRIKH8379CCZxcsEv/LXEUUJLbWFyddcqMkwjE+9Bgu0DZY2bU4MgiAgiiIej8za2lVSD1Sk1OLegsrqdGFzuZFNZkxWG6Io0W2pvRq1mWT6H1IvXc6XWpa78iAnKbw0t5FmosfZsvEya6tYvF5s3d2k5udaNu6D4ZxTw2N0ehtPohz0nmclfqOhfUulBgzJRhbsgoChazWaP+5FlGQEoXF14eTsm/hOfwlRauwY0WRBzSXRtTrvrYUOCL/HRTKVPnjHBjFKRQrRzYN3rEMmHsXhbV1Dx5NK22Bp0+YE4fV6icfX6OrqRBT3f8JKsoSmlhEEgcELT5BPJbG7PShmM9dSuX2PfRAdo+XelZNIK0tatVwWk7cDQ1UxWrh0zxSqPSRT/WMNHytLMjp63dBAuVTi6q05Lt+a48rthX3Him/HcXgPTiAWBAFJVrA6GzQGG/yoyukossW5UzHTON7TXyQ5+9ae11VNQxRaXNLcwhCMqXMEivtXmO2HWiyimFujL3SSab6ZRps2bY4NSZLYUYzAMFR0Xa870boCncQ31/H19JHY2sTfP4ggCPzvqyEGLCejWsAhiaRVDWcT5dWtRtX0lnt7XENDJJcWQVfxTEy1bFy75fC9ewDMso13Y8uM5nYMEqHis9F1A0ESOT05jEmWuH53cd9xkttxhs431tXYFdi/R9WD2J1eQuvTBHv3lvc/SHbzNp6Jl2tuSy3Mk56fo/dLX9mzTRRFFEeA1MIH2PvPI+1UFG0nN+lwt1bl1mmzkMoWcNmPZiiE3/vPaMlN/K/+ZQxNa6jNwGeVtsHSps0JQxBMaFoWv3+ceDyOv0aVUGW/nSRLwyCXSODr6eN2Js8vdHpJH6Cx8agImhUWcsVP1GCZ3kgx1edq6ZiCyQK6Si4UxdNcf8Ga6LrO27c+ZKJ39OCd92HId5GtjJULA40ZG7Uo5ovIpuOZGpyeTrZWbx1osNRC1zRC77yFo38AZZ8EZ0ffWcrZBIm7P8F/7msA5PJpepvMC6nF1ZszZNJZXnr+CVbWNgm2oFNzxxNfIrFwHcViJzV/Ffvg2SOP+WmlbbC0aXPCcLuHgMZCGO5gJ6lwCLu3Ik+uGQYBk0KgCU+6R5aJllT8xzRJfdJomoFSIwH5qHgmpnCPtcYwvDp3m+dOPYUiH/07aKRKaL89NmfXGTg7dOTrqIfdGSC8MYu/a3TPPZ5Zu0U5G8XasdfgCr/3HoEXXkSSZMq5HMVkAnMdw0Wxe3ANXSJ260d4T3++JQm3N+8ukIglCAY7iCZS5HMFHPbDqybfI710A8GoVGMZ5RySpbnS+89Kwi20c1jatDlx2Gw2crkchmFUKeDWQpIV1FLx/r9FQSBULJNvonzUb5JJHHCex5njTP5tJNn0IK7M3aTHH2yJsQIVrZ2jIIhCyyXsH8Ri85JKrJCOV5dC5zbuIspKpU+Qv9obktlYx9rVibSTgOueOkVqZnrf8yjODpzjl0jc/TFqYg21yZLqtfVtbt7dzfW5e2sak9lMoVjC7bDh8/uOfG+F3/6/yEdWcY4+ufNK83kxmdhnI+EW2gZLmzYnEl3X2draIhA4uFzW4nDer6Q4bbcwkyuwmC8ecFQ1xxk3d8sS8U+ooWKyUMZmOrmPudn1JQaCfXT5G88DOQi5AQXbh1nbjDC7XKlSEQ5xfDP4gn3YnT24/d33X9PVEsXUNrau2vG17MoyrtHdBGRRFBvyKigmF95TX6DDPsL8+9fq7qfVEJ2LJzMYksK1mxXDKJutJER3dQWQZRmb7Wi5K7nQCsWtWchGERUzhqbu6ZPUCGrps5FwC22DpU2bE4nP56Orq6uhla7d48UdrCQUCoLAS14nYzYzi7nGjRZZgNIxNSv0m2Ti5eaaMLaK5e0Mw0dUtz1OisUifpdn3320TIbC9EzDCqi97g7mIo2XyMYSKZKZHJ1eB1duzRMrHK8Ama7r5DLRqveTXvwAd50k23xoG7P/aLkigcFRFGvtSX0ru8V8cp5ksbpHVKlc5tx4Pxazmc3tKKPjo1itFno6/USTWZxNJNvmcotkstXtA8rRVdxP/RzlxXcppeOkF67gGH6izghtoG2wtGlzIpFl+UjuZpMoNuVc7reYTmSzwqMicLzhjcOysL5CvljgxuqdA/fVIhHMY6OUdzp6H8SQN8BSIrSvgZNIVkpob80us7Ed58z4AC6Xk/7eHnydx9tAL7a9imIy77m/631P6YV5PON7PS/FnE46vr+4YhV1fk8lrcSEd4JofreX17Wb03g8lUTtyfEhtkJRUskUdruV9z++yQfvfczQYONJvFbrABjVid+6ruIcOk/Pf/PbmOxuDK2MZPpseEoOy8n7Jbdp06YlNGPunCSRt88CGhr/11t/wKtnnm/sAEHAqCOTH42F97w25O5hLbX39XtcPDXC2x/foTvo5+zkrgz81laOgd7WCezVwupwUy7m77eg0MvFusZVfnsb2Vm7wkvpHmL9tT8hs74GQHppkdAH71PY2qp94jr3uIBArBDDZd49j6rqjA723P+33WbhbI/CxOgAzz51lgvnxpr6zQiCRH62iLqdgp32AXoxD4ComBBk5VA9hD5rfYfaBkubNp9StFZKex4RvyIRbkRdtYWkCmVsysnQo3kYr92N1WKmpB78mUheL8WZGUyDezsxb4XWiSUjRKLVCaybmTB9rhr5T+EZ2LyO22XnxadO4XNXh8ta1W9pP+xODxab/75HJTHzBq6xF6v2UQsFtt99m3w4hO9M7TJfs8tF8NUvUIrHCb//HrmNdYLPPEshl2X77TcJffA+22+9sXtAncm9z9lHWSvTYa14lgqFInfvVqsXT3Q7GLDsKtuazc0rQ7s7fYirP8DIVrxfis19f1sxvoVS6/s6gEwsiuOI4bLHiU9nHWObNm3wKzJzuQJjR0wObAVuRWYhV2yq3PqoLG9nOdvvPnjHR0wqm8GkmPDb/OR2Vtn7IbndSO7a7yOVSTIxcpqZxdt0+HeF0YR6nbdFCeT6k62hPxoj1zA0DMMgMfsWjt4LiKJIem2VwuYGCAKiJBF49vl9w3mCCIZm4D97rup1z8gojFT0bCIffQSArqpQZyxBEOi07352JkVmYKB391pVldJGDPPQS/dfq6eNtB+mHgd0fI2bP/ktAp192H27153fmsdz6sV9jq5NuVjE2TZY2rRp87jjVWRMgsBSvsjQMfQKehw47oqXwzC9usBSdBld1zGZZC7P3OTsyCQmef/uyQ+TSMZxOz0AuF1e1jdXyOTTWEyW+uFAQ9+3EuU4Pq9CrkBkZYu+qaH7r2XjBTaF9zHWMwhGjOTMItbubgKXnm14XHfgYFXgex6jYqGIydLYb0CUJDyeXQNR3d6mtLaGHAwiHdXgNtnodI0SuPgLD19omwZoh4TatPkUY5clJEEg16QGRZvj49LUefp9vYiCyO3VGa6t3qwbrtiPSGybzkAlz6LT343VYmNi+DS6qtCp1lH2dXaB1Vt3zN4eB3MLiaavpR6ZRJrN2RU6+oIs35hHU1XmL98l2DuGQwsgilbiH39E4JlncfTvDXkdncrnWsgWsDboaXz7nY8IhyP3c2yU3l5Mvb2U1tdbckX2/inykd0E6nI2gWxtvpJN17UTmVB+nHy23m2bNp9B+i0mNoqlT/oyCJpktouPJo8lV1IxHYO6basY7O7l1ckX8Dk8vHrqRUxK80v3h70hPm9HJbzh78Riq5M4a3aCvX4IweeykC2qFAut0c2JrYUZvjCBxWHDHfSyPr3C8IUJXAEP+a0tJLOJ4b/4l1pyrv3IxBI4/PXDg2+8e5WPr97m6s1Zunt7mJ2eu6+/AmAeH+dPii6uriaOfC22vlMUtpcoJyrJwdnlWziGzjc9TioUwhVsnX7P48DJ/UW3adOmZciCgPqI8hPq4ZAlso/I0zO/lWGs++Tpr+SLBWZWFrCZrKzFNxnrHmakp7/pcULhTTq8tScrm9NMIXt4w7DLY2blWujQx9fD0+lj4MwIoiQSv36NjqefRmiRum99KkZdqVDCXEeHBUCUZewOJxfPjjMy2MMrz7+Mw+yp2ucrZ7oIp6srmnT9cJ+z98zLRD7+/q5n7RBVeoahI4onM6n8uGgbLG3afAYYsJhYLXzyXpZHhgHSCfSw3F6exef2Mr+2QjgX5qO7Nw41TiaXxu2qH9o5LLl0kUS6SO+ol8idGJHpGPlI7vAD1pmH9VKJzLWfItvshx/7AHRNQ6+hYPswhmGwMjuLiH7/3wGvj54uH4nt3fduUST6BlyE8kkymWlisXfIPiQG1/C1qSXsfacpZ+NIhwgHVa7zUIc91py8X3SbNm1ajigI7by+E8C11Zu8e+cjfufy7/L86CX6urqO5TyabhAOZZo6Rtd15pcTTE50YOuw0nHKR8ekj0y4fiWTruus3FygVC/kaIBWLLD99lukt7bQdY308hKiyUwhksB7+kxT19gMejaH4jhYU+a9j25w5uI5JsYqejSFTBm1pGF3m/cYXNGyilbcQFXTyLIDSTq4+eHDlVeF2Cap+avINgfZlTs4hy80/qZ2KOaymG0HJx1/2mgbLG3atHlkPIqanbKqcwKdKwD8N1/4CyxFV/h/PvuX+L0P/pCZ7cOt0A8SLZuc7EDTDO7cCTMzE2V2JsLcbJS52ShXrm1RqiFCd+d2mFNTe7VAxDqVQ4ntGEvXZumZHGDt9mLNfTRVo5zJoBULhP/oD9n6yY+JX72C5/QpNEvzpcHNoBfyCFYr5XwB2VQ/R8jQq0XirE4TgX4n4dU0xVx1Ls9LXidC4Q52+xiCaMJs7n54uN3z60Ui0ddJRHcTbHNbC2Rn3iV/+08w+ZsPBd4jm0hg97Tew3bSaZc1t2nzGcEiCuQ1HetJnc1bxOxWmvHu41VrPQpfOfcFeoNBssUs19duE0nGWAtvsp0K84XzLyE3kNfRiMJpV7eTrhqfg65q3LwdZnTEh91RmcjD0RxdQQeyvPfeqHemZCjOyBOTAEiyTKlYwmTeNQwSoThWlw2LvwO9rGIbHSW/tYV76jTZtTUk28HeiaOQj4SxDQwgW8xo5fq5Ji6Ph0Iuj8O+67GQFJFAv5P4VnbP/j7/qyiKB0Xx7Ht+UTSjyB7WM7cJxwv4NR9LupnAle8i9Z6nlElgcn72jI6j0DZY2rT5jNBtVljMlxixfbo1WTTNQJFPbjJiupgim3My0j3IO3MfsrS1hsvm4EsXXuG9u5dxWh1sJ0NM9U3QF6gdMjqKIq0oS5w/38XMTBSLRWZgwE0ilmd8vLbHo54v50EvT/+ZYVZvLdzbAAaIkkj/6WEA+r7yVQDWXvsjknduYw50YOms751oBeVcHpfdcaA3KpVMkkmnKWkGPV3VFVTerr05NmZT40JtKysS3T1P8ePUJroioC2/R+fgE3R98b8lPXcZz+nmxeLUUglZaU6z59NC22Bp0+YzwkEPbh2DrKphP8GTfSOcPKm4au5uzLIR3+ZU3zjfeOHP0OXbDcO8cOZpAM4xxWpok9m1Jcb7hqqOV1X1vkbIUZiY8JNOFbl1cxtV288Aqv2JSopEaUeQTRRFBs+NHXhOz+kzhN74KT1f+NIhr7o5Gun388IzlRySK9en9xgsR2FreZlAIICAyNPTHzDzxJ9nYHsL16nnkUxWDqsWl4qE8Xb3HLzjp5BPt2+4TZs2Vey3MreKIuJj3gSxFRP5cTMbXuD2xl3evPU+uUL9bsP9wW4yhcye8M/c0h1GBydbci1Ol5kzZzs5c2Y/PY/a94xhGE0LlzkGBun/s7/Y1DGtoJEQWr5QIJU+QkXUQyxeuYKqqgjFNJ7OQXyhu/htDgrrt8iFVzH7DpdwLQif3WalbQ9LmzafIbrNJtYKJfose5MQu80KS/kSw59QyKis6cQyReLpUtWK/8Gp5uHH9P3n9s5/s3mVc0OeY7zKo9Pj7GItuUH3UIBiubjvvgNdPaxvbdPXXZncNrZW6fB1tlzhtFbuykFoZQ3Z1HxoQnlE1S2GUUksDm3H8HUc3FPqySfOsrSwjGtyZM+21fQqJa3EqGe04fOXvF4EoDz7Fv7nf4m+P/jHeD///0JxBUnNvIf7EL2DNLWMKD3eHtCj0DZY2rT5DGGTRFYLOqpuID9U/SEcc+lzuaSxuZoia6k9yYmigMdhYrjTgfmEdlluBX/uuZ9lOxnhVP/4gStlQRcp6EU2o9tsrm7S2xOkw/fZUjc9DIZhYKiVCp/0+iajTx5cPi0ZGmqN8NcbsTSTZhNBW3Ofu8PhIJfPE+waQzaZMCweSskQJm93Qx6fWiRDoc9sOAjaBkubNp85JmxmpnMFpuzHW6XxMIVMmWdH/EjKZzsS7Xf78Lt9De0bTyfJF/M4bXaGLAP4go0dd9yEV7Zw+uv0KzoBxK9fw3P2PKVSuWGBtbuzy0xOjlRV0l1OZvEqUlU354PIvPkmhbExJEmmGEujSWHyoWV6f/bXAciuTWPtPjjfpxaf5XAQtHNY2rT5zCEIAn5FZiFX5FYmz0KuyHyuUMlJEEA/JglNTdU/88ZKs4z2DnJuZAqxkEIj+UjPrZY0rn33IzKZ3dLe1dsLLN+YB8DXs1ez5aRQzmYxOZ2s3Jhm5IlTDR2jGwYmWeIPwwmW8pVQnUuRms7rUrq7uf7OFaKrOazlIJGNBGbvbkVUORXG7G0+f0XXNITPmBT/w7Q9LG3afAYJmBQCD6SxlHWDu9kCQ1YzW8UyPTVyXNp8MqilMrnwLL2TrzzS8y6+NcvIyxNEVytN+rYW1vH2BHB4WqdxoxdV1FgR2W1CtLWmVDd27Srec+cAECW5oXwfwzBIZ0LABOedNqyiyGy2wPdCCX61vznDzNB0nvvcyyR1EQkDZW0AQZJ3tqmH9pAkw9t4gsejjPy40F7utGnTBkUU6DYrZDSN4ifcJLENlIu7cvjb63fw+geRzY82hJfP5nD6XPczmzv6gyS2oi09h5YuY+q2o2Va18W74l1pLlx19cY0fl8fuq5RzqTpNCtIgsBf7Q80XeZvmZzAJCr4CzrSW9eJbc4Qv/UGAKnZD3COX2pqvPsYBkKLk60fN9oeljZt2gDg2QkTtfnkuf3xd/EEBrE7e3B6AljMdpIrNxEkGXtwGEk53kouXdXvN48MDHaxeG2GcqFEz8RgS88jCDu9durI/zdL9OplvOfON31cvlDkifNTAKiWimGoGQauQ2oS6XmVcjyPOFzGGxEQtcrvytB1RLl576Wh64fq6Pxpo22wtGnTpor2Y/GTQ9d14pF1ugefxuZysbF4h4nzrwJgGjiLruskl69hsruxB/eW37aK1HoKS8ACgN3tZPjC8bQ6kHwW1O0ccqA1pc5qNo/JuXutjczxuUKJfK6iv7IYyyIrImlV2wmNKtgPUUasdNt5Pf8mI8urBINT6JQoJsIojsNJ8SdD27iDjSf+flr5bPuX2rRpU8VRJN8fR0rFMulk68TCjsrMzAxXr76Jv2sIuyPA8KnnqraLooh3+Akkk4NsdAUAtdBcV+ZGEAzhkVSjCIKA0mVHkI5+rsiVK3jPP+RdaSCBXJFEivEIHyeziFaZkmGwXizRbVHYLqoHHl8LQRAYM3Vgl6zoErjOfI78xgz2gcN1p9Z1HfEznnALbYOlTZs2D5BWdZzHJM1/0jza4a04G8shlmbX+OPvvFGljZHY3qKY29v4rlWUywXWFq6ytXqb0PrM/denpqYYGZokn02TzySRpNqJqBZPEDWfppRJkFq/Q+6BjsCtwDngJB+ur8J70jAMA62Qq/KuABgN+At1XadvbJxYWUU34JTdypTdilOS8B5BD8ifLWPrHCW/cg39gZykZtHUMtJj3i6jVbQNljZt2tznlMOC75hE246pWvpQrMxvoJVVioUSS/OrvPTFS/c9CuViAUmWySYSx3LuVHyL8PoCvcMX6Oo/jdniJh7eNTg83i4S4UWuvPstbn/8nbrjiKKJXHQJ3+gltFKRYirSsmsURfFkfWEHELl6Bd9DuSv5XAHpgNyY9e0Y80vr2B0Ovuh3IYvCfQ2WTrOCV2ksa0LXtEqeyQOUdjxfaniBQnQVW+94o2+nimQohCvQDgdBO4elTZs2D2D+lFchrC5sUi6pWGxmuvoDOD0O7A4rduduBY5aKqGYLZTyxxMqyqXjaFoeTdOQZRm3v5PN5dsYhsHC7TfpGb6AYjKTjJ+ho6u+hoizZ3cCdHaPkVi+jtnVuuZ9unHy+zJBxUOi5/IojmrvyvrtOUafqh2CWVpeZ2Vti4nhXtRyiXw+z0bISn9n7Y7VB5FPp1AsFkw7CbtX3nsNWzZD91AHgtmOmo7j6D99qLHhsy0W9yCf7qdTmzZtTgSGbnziD910MousKPQNd9EzUJFZtzutlMvVeQpWp4tUJIRiOZ4y4nRynVR8ma3VmwCEN+ewOwPEQos4XD3M3fg+iegaDlcQX+dAw+OanZ0NeVlisRjZ7MHhrp5Tg8y9O93w+T8pErdu4jl7tqrxpaHrIIp177l0tsArLz5FV08X/f09xGIJ3I7Df982t4dSfnc6fR0r4y99Hdfok/T+ub+LcMgqqHwmjcXhOPR1fdpoe1jatGlz7BRyZSz2T/ZxE9mKMzzZt+d1xaSwvRGls2d3dR0cOr4KHFXNs7X5AxTTnyWXTZBJrqPaykiySFnN0z1wiWIxSTK2gsXmoLPvbEPjlrJh3P3773tvUi8UCtjt9n339Y/42b6zTmhpk+BQ9777fpKouRyLtxZQrBbK+QJTLzzB6s1Zes/UD8E8aD5YLWaGhwdw2A9fqaTnVBze3VLz/0rJIuoliG6S3VxA804S39rYOffu2e8luQsP5drce13XNPy9/Ye+rk8bbYOlTZs2x04xp+IOPFrhs4epl5LRN9zJ7O0l1pe2ePKFM+SyBQTAarccy3WceuLnMJk9jJ5+GQCLLYihFwmvz9M18AT+zhHKpSILd/8LmfRCwwaLIB78ODeMiqdLVRurfpn62llufPfyiTVYdE1jfXqJ4T/1NTw+N7lUhrvvXgVRwmqur5xb0rT7/28xmxgb3mvINkq5VCSTjkK68u/Z6AwXO3tBV8E3gsPfeIfnNvvTNljatGlz7JyEpm3CPgFwh9OBWtbQdZ271+YpFVV8HW5MFpmB0Z6G5N0bRdc0Vhe/y8DYMygmM5Iko2oahUIST0clBLQ88w754l06/F9qeNyHV+m1kCSJcrmM2dyY8Jwoijg73MQ3o8z86BbjXziNr7t1eTJHZWZhlfFf+gUclsr7sbkcTD1/8cDjpEOU7xu6TjoWQSuXq0reZJMJT2f3/fv7ma7Pbjfl46ZtsLRp0+bY+aQLTgzDYHF6lVQ8w9jpwaokW4Du/g4K+QLZVB5fwMPQeC8A5bLKjY9mufDMZMuuJbw5h6bHiW7N0TVwhuBO9UjP8K4npW/kaYqFFLp2OB2Q/QgGg03t3322j1uvXebCzz3DzBu38P3cyTFYciX1vrHSDNlc/r63qRaFbIZ8KrkjxWuAICAg4PD5kU3tPlufFG2DpU2bNp96ctk8Lq+Trv4A09cX0XWNqfOjONy7eQtD4738/v/5GpIo3TdYFEUmEUuyvR6ls/dwFSQPIxhenM6n8XfWz5Ox2JycefrnKRYa14I5LpvQ6rXy9F94EQCn10NsM3IivCzZfBGz6XANE8dH+lnfjtET9JCOhNE1rcprYrba8Hb3tupS27SIdpVQmzZtjp1PuioztBmlI+jFYjWTSqZ58sUzbK6HKJd2PRiCIPBz3/gimq5VHaupWsM5H41w640Y/uAUiej6gfuaLfsnxlbxCMqQ7X4n+fjxCeo1w83ZVU6NNp6QGskUWYpkWYpkSekKajJCKhzG4evA292Lt6vn/p/N7Tm+C29zaNoeljZt2hw7n3RIqCPoI58tEt2Oc/ZsJ+VCHn+Hhyvv3uaZV3cFx0xmhbJargoXGGjY7K1JGC7lVXxddsKz/WgDMXzBMpLcmJdgOV9k0Fo//CHsl6TTItKRFMEzgWM/TyMokrAnt0jXDbZSBUpqtfFmAH6HiaGOBwzAzuY6Orf55GnqDv/Wt77F+fPncblcuFwunn/+eb7//e/f357JZPi1X/s1+vr6sFqtnDp1im9961sHjvt7v/d7nD59GrPZzOnTp/nOd+qrO7Zp06ZNszjddjbXQqQSWfx9A2QTCTwdLswWE6mHPAY/89UXyWV2Zelf/vKzpBLpllzH1kKSzfkEDo+FQspGuVRq6LiVfBGfIrOSr99N2xAMdO14vSwd/QG257fYnF+t0j151GxH4qQ16b7H5N7fajyHz14xTB78G+6w47IcLnzU5uTQlMHS19fHP/pH/4iPPvqIjz76iM9//vP8/M//PLdu3QLgN37jN3jttdf49//+33Pnzh1+4zd+g1//9V/nu9/9bt0x3333Xb7xjW/wy7/8y1y7do1f/uVf5pd+6Zd4//33j/bO2rRpc2L4pENCAKcG+7nw9ASCKOLr6UUURS48O8Xa0iblchljJ6QSDye4dXm3v0+pUCafbcywOIi7720x9UI3Z1/tIzBkxmKrH/JZ3p5jMVQJG6kGOGWJvF7bVWXoBoXUFnq5vkHTCpwDTpK3ouRXikz/+OaxngtAU1US21vEtzaq/iyovHx2cI9hMui3Yzmm1hJtPnkEwzias9bn8/FP/sk/4a/8lb/C2bNn+cY3vsHf+Tt/5/72p556iq9//ev8/b//92se/41vfINUKlXlqfnqV7+K1+vld37ndxq+jlQqhdvtJplM4nK1XX1t2pwkEqEcnuDhhbmOihovoOdUZK8Z0Va90tZ1nZlbb9I73InTMUU8kuLNH3zAU8+fo1goYbNb6Oo/ehgkGcpRLuvYnCZsLhMbS3fpGZoiHlrFG6zOxUhkYiyGN/DZPQwG+wiXyoRKKkGTTKBGomk2tIRoMmP1PDq9lFvfv8qZr11syVi6rpGORCpVUQ9Yt6Io4ezoaHcq/pTT6Px96KCnpml8+9vfJpvN8vzzzwPw0ksv8b3vfY/19XUMw+AnP/kJMzMzfOUrX6k7zrvvvsuXv/zlqte+8pWv8M477+x7/mKxSCqVqvpr06bNyUMr60jyJ5vfL3stKEEbok0hnixUdWYWRRFRNCMKlZS+dCqDYRhsb0QYmepvibECkIzk6eh1YHNVymKzccgkoyzPv1m1n66rzG+vY5ZlBgKVSpWASeGMw1rTWAGwB4coJkItuc5GKMbyiPbmQyyGrpOKhIlvrlf9VZJffXuSX93Bzrax0uY+TSfd3rhxg+eff55CoYDD4eA73/kOp09Xmjp985vf5K/+1b9KX18fsiwjiiL/+l//a1566aW6421tbdHZWd2JsrOzk62trX2v4zd/8zf5n/6n/6nZy2/Tps0jJpcuYXN+8toVglIxmj6+HcbrtvDU6Yohsji9RiKWoHeo8u+BkR4+eP0GlkPoe+zLA77sxesRRMHNzI0/wOwb4b3py/T6u+jv6OHq0m0uDp1lemOmKbE9W8cgydWbB8rzt4LIUgxfv6/udsMwyMSjqMUHQlRtLZM2R6Rpg2VycpKrV6+SSCT4vd/7PX7lV36Fn/70p5w+ffr/3969x8ZV3XkA/94774dn7PHYHr/jOuTl1DhAGpLdJYgCLVsKWylbtbBFqP+UVilITftHUBAqFa2qUgTlH9RCE0VCG1bdZRvENjTuNulSWhpCQwJ5EMdO/PZ4PPa8n/ee/WPiSYxfM/Yd+9r5fiQr8syde8+dkzPz83n8Dn7xi1/gr3/9Kw4fPozm5mb86U9/wne+8x3U1tbi7rvvnvWcn26UcyX0mbR3715873vfy/8eDofR2Mg9F4j0RsmqMJiWt4fl8kAYPQNh2G0m1HrtGJ1IIptVYTTKsNhMGOrLwOVKYcPNuePv/9qd+O9/fxubblmryfXT6SxkY+4z7e+dvbDYjIgGk3DWVeKT7lF0dHTgsv8cxiIBtFQ3w2CQi15ZZXaWIzHer0l55xMcGsHmji0AgHg4hGQ0OnWekiTBWe6BybP8+Vpo9Sg6YDGbzVi7NteIb7vtNpw4cQIvvvgiXnjhBTz55JN444038KUvfQkA0N7ejlOnTuG5556bNWDx+XzTelP8fv+0XpdPs1gsBaeXJqIbm6IKbLnJC5fbDFmWEQwl8beP/NjR4UNdUw28VfUYGgjnAxaz1QSjwQhVVTVJyz/cFULDhgoAgLfeCW+jEz2nAnA1bcXFgeO4zZnBPzVsw3h0HBVON1RVIBgNFX2dQvYT0oJQVUz4hwAhYCtzw1PHJGtUeotuiUIIpFIpZDIZZDKZaY3bYDDMufxt+/btOHr06JTHfv/732PHjh2LLRoR6YAeVgiNh1P5YAUAPG4rvG4LIvHc6h93RRlabrrWQ3vsrffgrarE6OCEZmWYvHY6lcW7//U+6m6SMRYO4r7NHegKDuHNcydgMuc2XAyPfAJXqAvnr5zBqZ4ziKcSBV1DzaYR6juLxMSIZuX+tHQkDU9DU26eSW09rE5nya5FdL2iwvEnn3wS9913HxobGxGJRHDo0CEcO3YMR44cgcvlws6dO/GDH/wANpsNzc3NOH78OA4ePIjnn38+f45HHnkE9fX1+MlPfgIAeOKJJ3DHHXfgpz/9KR588EH89re/RWdnJ9555x1t75SIlsVyJ40DgKwipv0x1dLgwofnRiBHJtBx+wZcuTQIAOjrHkL9Gh+G+0dR0zD7PI2iXBe02ZxmmExGKJk0Xn7nVXzz9kdw5/qbIYTA3/q7kFUVZIPd2Hbrv8Ia6oFaeRMuDF7Exob59zMqb25HdKQbBlNpdprOprPo/9swfFt9JTk/0VyKClhGRkbwjW98A0NDQ3C73Whvb8eRI0dwzz25HUUPHTqEvXv34uGHH0YwGERzczOeffZZPPbYY/lz9Pb2Tvng2LFjBw4dOoR9+/bhqaeeQmtrK15//XVs27ZNo1skohudzTx9pYnJZMCYP4S7dq5D/+WR/KTYaCiO1rYmeKu0CVZURZ0y4bbvXBAtN3shyQY8cttDaKnN9exIkoRtjbmNEFW7jHe6P8Ad9S2Q5eLms1hcNUiOD8DscGtS/klCCHQf7UXzXY2w2JiEjZZeUQHLq6++OufzPp8P+/fvn/OYY8eOTXts165d2LVrVzFFIaIVQg9DQnlKFhi/DHhz8/AymdweQVe6+tFxexsAIKsoMJtNqPRp84U/2DWB2rXl+d+d5RZY7BL+fuU8gtkYtrfdCgAIRAIIhAMAgEgiCCU8Bmy4PfeiIiIWk82BeCANVVEhG7Sb7CxJEmSLgcEKLRtufkhEJaWHISGz2YBk8mqw4qoDQrnVNLLBAINBhtlqRtA/AQBwlGmzb9AkJaPCdF0PT+/ZIDJpG1JGgZ1t1+bqXQlcQXN1M5qqm7B17Q6Y7JsRSy4sw67d3YJEpHfRZb+emlFgspugzpJtl6jUGLAQ0aq3ttGFS33hXHeP2Q6kc/sHXRmJQZZlVPsq84sDZA03EYyOJ2ErM+P0H/sAAN0fjmLLvU0IjyZwZbQf5we6kMlkcgdLgM1kg92Uywjsddlx+N1c+nuj0YJwvPDkmJJkhBDK/AcWKJ3I4tL/9iF2JYxgnzb7KhEViwELEZVMLqfScpcCGBqNw1dlA8xOYOg04G4AALTU5la41DZVIR5LITg6gUXuVjLFcE8Yf3njEqxXs8IOdU3gg7evoG5dOXpjvbBbregfHcE7F9+B1zE1Z8mGphrUecsBANXuKnQN9xR8XVWkYJCnTrxVsioCZwIIng8WfR9DH/hRu80H7601iA5Gi349kRYYsBBRyaTiWVjsyz/noarChlMfXkFEsQG17YDZAf9oDFZz7iPQarPAajMhMDyBrKJdz8T4YAwVtQ64q+04+uuPUVnnwPZ/aYW7yo77NtyD2266GREliPXV69Fc1Tzt9TVuG/oDISRSYQDX3kchBKITYxBCQFEy016nqknIsuW631WMnvLD0+aBs6EMox+OFrzbsppVERuIwlluRfW6CqzZXlf8G0GkAQYsRFQyqXgGFvvSJDObi9VqwJmzl9HXlZu78tr/XMTQWALNNdd2S66uq0Q4FIVBg0Rxk7be3wJvvROpWAbbbjfDGhtBhS93zXJHOT4e+Bi+Mh+q3DPvV7ShuRZvnTiD84O92Nx4LevucM/HyCTjGOk7j8BQD4Z7z2G49xz8A5egqipURYEkXZs3E74cQeWmSsiyDLPTBM/GCox9NIbEeHLeexAQcDQw1wotv+X/JCGiVa2Y/XBKWYbq6goYjLkv8c2fqUAZcpsgxiIJOMpsKHM7UFPvhUEyIB6Lw+7QZnfpTf9Yh0w6C5O5Es7W3N46qWwal4PduO0zHagur571tUO9AWzxxbG17Z78+zg6dAlmhwsVNdO3IslkEhjpOwclE4clUYeqz1YgOZFENpmF6bqeLoPZiKr2KoxfCEIIgeRoLjGd1WuDvXLqpOPRUwG4W8sX+zYQLRp7WIioZPSwQmiSw2aB2W5DMhpDKq1AknJDK0f++xje/s/cjsmNLT5U13kQGCk+Lf5cTObc34by1e1EOt8/jpub21DnqUNwNITxsekTagcu+5FJZdFUWT8l6MumE6isaZr5OiYbapvb0LB2K8wOB8L9ESQDSXg3Vc54fMV6DzLhNNxry+FZ70E2nsX4xfH888ELQRiqbCiv0SZ4I1oMBixEdEOwWC3wVrrQfeEKrBYDIAHeGg8cDjsUoeDD984DAIxmA9RsYfM7FmqLrwOJgVw0d+njAVy5MH13+mQshaabfLj+Y3pibBA2Z0VB1yhrciE2GEX5dTlgZuJe44bhar4WV2MZDCYDYsPXJtaabOyIJ33g/0QiuiHU1Veh60IvKqs96PGH0L6jFQDQftsmJGIJGIyG/E7xWq4UmrEsa6rwt1fOIpXI5Vn57OdapzyvqipCw0GgrRFAbhJwJpVEMjYOX1NbQdeQjTJqP1dbdNlca1wInAtCTYdhqbBi+pReouXBHhYiuiFIkoRENIGmtQ04dmYYbx6/DACoa6pC68YmJGMpCFWg50I/Kqq0TWs/k233tmHDljUwmQ2Q5KnzfCITcXjX5ZY5J+O5nDH+wU9Q3bCx5OUCgMoNFQieDcJRzaEg0g8GLERUMjqYb5snhEBldS4Q2VBpQFWFFf9zwZ9/fmRoFLJBhhAC5Z4yza6bVlV8Epu+GsdoMuD83y8jm1ambcwYGB5HY0Nu+bDTVY1LH/8fTGbLtOMWKpNSMOGPY8Ifx/hwbFr2WkmSYLAZERuNa3I9Ii1wSIiISkYPk25DowkIIWA2GyFbPUhdvox//GwNQg4rsuncFzcAtG5qRjAYhNlswsRIHLJRQpnHuuhVTgZICI7EoKyxwHBdT8rpv3Th1p3rUe6aOU/N5HW9dS3w1rUsqgyTouNJZFIKTBYj3FW2/PBXyJ97j5wea34bAW9HFUKXQrBwSTPpBAMWIlqV0sksosEUXF4rjGYDynFteKP+6s/1Jp/3XN2kWcmoCF1d7gsBOD0WGE3Td32ez1AoAZfVhBf/cBF3baiGz2VFKJFBSpVgSw8BwSxQuRYwXkv0JkkSFEWBwVD89T5NCIFwIAlVUeEot8BZMTUDriRJ+VVAkWAS0XRuOwGr2wLrLdX5gI5ouTFgIaKSUDIqDMblGXUOBxKABHjqHPMfPAuDSUb51TkcQghEx1NQsrk8KhabEbYyc0HnySoCa7x2uKxG/LkrgC99thbrfWUYrCqDxWwEPOuAwCdA1fr8a5rW+tB9th83NQigYs2Cyq+qAuGrvUsur62guijz5IKZ6HgS0bSSC2500EtGBDBgIaISiUfSsBf4pa6VTFpBJJBEWaUVJsvieycmSZKU/zIHchl8xwajqKybf7hEAEikFbRWOSHLEqpduZ6UMm8ZrnT3oVmSAffUJHBGkxGhsTjCjY1wFVnWbEZBZCwJSZJywz5y8UNazgorktEMxodjMFv5NUH6wEm3RFQSSlaFwbR0HzGRYBKJcBqeOoemwcpMLHZTwb1HEoBANA2TQUaTxw771SRyWzbW4oOLwERMzu0gfZ1YJI7gSBjJeKrgMqUTWUyMxBEPpVHhc6C8xr6gYGWS1WlChc8BR7ll/oOJlgADFiIqiaVaIZTNKBgbjMJiN8Lltc3/giUmAKz3lSGSzKDFe22IyiBLkEwyIulrAcEnp68AAGTZgHAwMW31zmxSiSwS0QzKa+y6fA+ItMC+PiIqiaVYIRQJJqFk1YKGZrS0kMRyd22cumfQqb4JeGvduPTRAFRFoHldLS6c7MPl80NoXufDFx7aijJ3YXNwouPJJX8PiJYaAxYiWnGUrIqJkTicHissy5A6PhXLwuqYeTnyp00GNxbj1GEqt82EqoZyhB0mGCTg5PFzaN5YA4fLitZN0zc2nM3ESDw/OZhoNeOQEBGVRKmGhKLjKUTGkqisdy5LsAIAyXgGFnth156tL8ZjN2PcZMGGjjXo/I/TMFoMaNv6maKSwyWjGZishmVbjUW0lPi/nIhKQushIVVRMTYQhdEs62L34EISys01dOS2m2AzG5CMp9D2D41o/EwNIqE4IAqL9IQQiIVTcLg5KZZuDBwSIiLdS0TTSEQy8NQ5Fp15din1BuNo9sweXBkkCSaLEdm0gp7zA3CVW9Cyoamgc48Px1HhW3ieGaKVhj0sRFQSWsUVodE41KyAp1Y/wUqhxYilFPy1Ozjr8wKA0WjEuD+KW+/YhJvaW2E0zz83Jh5Ow+owQV7EsmWilYYBCxFpTgix6CEhJZsbArK7LSs2F4gqBAYnErgciM34fCSZAQD8879tL/ycqkAyloHdtbRJ+YiWG4eEiEhzqXi24EmpM0lE0kjGMqisX9lLdTfXu7G53o1QPIPT/RNobygHAGQVFf5ICvXlxedMGR+OwVPLoSC68TBgISLNpeKZBScwmxiJw2Q1rJr5GR8NhJDKKqh12xBOZnA5EINBluB1WlDpLK7nKDaRm2Srl6ExoqXEgIWISqLYL1Ulo2LCH4e7ygajubSp9RdDVdSi7q2tzoUufxSyJGE8PIj2huYFXVfJqsiklBU7PEa0WAxYiGjZxcNppOIrYwgoHk7D7i58/ogQgEOREBiKocFThQl/fJ4XXP1Xmvq7oix9Rl8iPWHAQkTLamIkDrPNuGKGgJSsCqPpWg+QEAKJSAbpZHbqgVcDDUmWUOW1oY7BBtGiMGAhIs0VskIom1EQ8ifgrrZNCQD0TjbI03pJ7GVm2F3Ln8yOaDVjwEJESy4WSiGTVFbEENCnlXmsy10EohsS87AQ0ZIaH45BkiRdpNcnopWDPSxEtCQy6dwQUEWNHQYT/1YiouIwYCEizX161W8slEImpcDbsPKGgIhIH/hnDhFpSlFUSFf3uBFCIDgUgyxLKK/mEBARLRx7WIhIU8lIBrYyMzIpBeFAAuU+OwwG/m1ERIvDgIWINJXNKBAxQMmuzFVARKRPDFiISFOyQYbBJMFZwSEgItIOAxYi0hTzlBBRKXBgmYiIiHSPAQsRERHpHgMWIiIi0j0GLERERKR7DFiIiIhI9xiwEBERke4xYCEiIiLdY8BCREREuseAhYiIiHSPAQsRERHpHgMWIiIi0j0GLERERKR7DFiIiIhI9xiwEBERke4Zl7sAWhFCAADC4fAyl4SIiIgKNfm9Pfk9PptVE7BEIhEAQGNj4zKXhIiIiIoViUTgdrtnfV4S84U0K4SqqhgcHERZWRkkSVru4uheOBxGY2Mj+vr64HK5lrs4NzTWhT6wHvSDdaEPS1UPQghEIhHU1dVBlmefqbJqelhkWUZDQ8NyF2PFcblc/EDQCdaFPrAe9IN1oQ9LUQ9z9axM4qRbIiIi0j0GLERERKR7DFhuUBaLBU8//TQsFstyF+WGx7rQB9aDfrAu9EFv9bBqJt0SERHR6sUeFiIiItI9BixERESkewxYiIiISPcYsBAREZHuMWBZhY4dOwZJkmb8OXHiBADgwIEDsx7j9/tnPXcqlcJ3v/tdeL1eOBwOPPDAA+jv71+qW1txCqmLSQcOHEB7ezusVit8Ph92794957nvvPPOaef82te+VsrbWdFKWRdsF4UrtB5mev7ll1+e89xsE8UpZV2Uok1wldAqlE6nEQwGpzz21FNPobOzE93d3ZAkCYlEAqFQaMoxjz76KJLJJI4dOzbrub/97W/jzTffxIEDB1BZWYk9e/YgGAzi5MmTMBgMpbidFa2QugCA559/Hj//+c/xs5/9DNu2bUMymUR3dze+/OUvz3ruO++8E+vWrcMzzzyTf8xmsxWUMfJGVMq6YLsoXKH1IEkS9u/fjy9+8Yv549xuN2w226znZpsoTinroiRtQtCql06nRXV1tXjmmWdmPcbv9wuTySQOHjw46zETExPCZDKJQ4cO5R8bGBgQsiyLI0eOaFrm1WqmuggGg8Jms4nOzs6izrVz507xxBNPaFzCG4dWdcF2sTizfT4BEG+88UZR52KbWByt6qJUbYJDQjeAw4cPIxAI4NFHH531mIMHD8Jut2PXrl2zHnPy5ElkMhnce++9+cfq6uqwefNmvPvuu1oWedWaqS6OHj0KVVUxMDCAjRs3oqGhAV/96lfR19c37/lee+01eL1etLW14fvf/35+13Kan1Z1wXaxOHN9Pu3evRterxdbt27Fyy+/DFVV5z0f28TCaVUXpWoTq2bzQ5rdq6++ii984QtobGyc9Zhf//rXeOihh+bs4hseHobZbEZFRcWUx2tqajA8PKxZeVezmeqiu7sbqqrixz/+MV588UW43W7s27cP99xzD06fPg2z2TzjuR5++GG0tLTA5/Pho48+wt69e/Hhhx/i6NGjS3U7K5pWdcF2sTizfT796Ec/wuc//3nYbDb84Q9/wJ49exAIBLBv375Zz8U2sTha1UXJ2sSC+2ZoyT399NMCwJw/J06cmPKavr4+Icuy+M1vfjPred99910BQLz//vtzXv+1114TZrN52uN33323+Na3vrWwm1qhtKyLZ599VgAQb7/9dv4xv99fdPfp+++/LwCIkydPLu7mVpjlrgu2i5xSfT5Neu6554TL5SqqTGwTy1MXpWoT7GFZQXbv3j3vjPc1a9ZM+X3//v2orKzEAw88MOtrXnnlFXR0dODWW2+d89w+nw/pdBrj4+NTIme/348dO3bMfwOriJZ1UVtbCwDYtGlT/rGqqip4vV709vYWXKZbbrkFJpMJFy9exC233FLw61a65a4LtoucUn0+Tbr99tsRDocxMjKCmpqagsrENjG7UtZFydrEgkMd0j1VVUVLS4vYs2fPrMdEIhHhdDrFSy+9NO/5JidSvf766/nHBgcHObmwAHPVxYULFwSAKRM9x8bGhCzLU/7Sn8+ZM2cEAHH8+HFNyrxaaV0XbBcLU8jn0/VeeuklYbVaRTKZLPgabBOF0bouStUmGLCsYp2dnQKAOHv27KzHvPLKK8JqtYpgMDjtuf7+frF+/Xrx3nvv5R977LHHRENDg+js7BQffPCBuOuuu8TNN98sstlsSe5htZivLh588EHR1tYm/vznP4szZ86I+++/X2zatEmk02khxPS66OrqEj/84Q/FiRMnRE9Pj3jrrbfEhg0bxJYtW1gX89C6LoRgu1iIuerh8OHD4pe//KU4c+aM6OrqEr/61a+Ey+USjz/+eP4YtgntaF0XQpSmTTBgWcW+/vWvix07dsx5zPbt28VDDz0043M9PT0CgPjjH/+YfyyRSIjdu3cLj8cjbDabuP/++0Vvb6+WxV6V5quLUCgkvvnNb4ry8nLh8XjEV77ylSnv66frore3V9xxxx3C4/EIs9ksWltbxeOPPy7GxsZKfSsrntZ1IQTbxULMVQ+/+93vREdHh3A6ncJut4vNmzeLF154QWQymfwxbBPa0bouhChNm2DiOCIiItI95mEhIiIi3WPAQkRERLrHgIWIiIh0jwELERER6R4DFiIiItI9BixERESkewxYiIiISPcYsBAREZHuMWAhIiIi3WPAQkRERLrHgIWIiIh0jwELERER6d7/A5+TvWvEB8RVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"for some states like FL, we move in partial counties before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4bcdcf95-9358-4921-8d58-22e3b5a4aa95",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#new for MD as FL used the below approach instead; this is the first true squished state\n",
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "78ad4c0f-eb6e-41bd-9166-0c9bd3c57d9e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the faint(original) and squished shapes for county 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 22\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "73dbac1b-4bf3-480a-b028-6edf55a5447d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alternate simple approach for Florida Keys - translate all toward county CP\n",
      "I translated 28 vtds toward the center of county 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ecc512e1-cb48-4c0b-90f4-b90458ee23f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use shifted shapes (e.g. for MD), else enter 0 for most states to use uncut counties 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 501 is inside the map's convex hull\n",
      "working on confirming HD center 1020 is inside the map's convex hull\n",
      "working on confirming HD center 1527 is inside the map's convex hull\n",
      "working on confirming HD center 2037 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD we leave CCB's as individual vtd's, draw HDs for ALL vtds\n",
    "#Can draw for each vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use shifted shapes (e.g. for MD), else enter 0 for most states to use uncut counties\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[0].buffer(0.1)\n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if tractPop[t] > 0:\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "650ae996-d3ca-422f-8ab0-3ffa3cf6eb67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "54f09a52-c279-4647-9860-0729d1912b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\n",
      "working on unit-unit distances for unit 0 out of 2042 . Time = 0\n",
      "working on unit-unit distances for unit 401 out of 2042 . Time = 31\n",
      "working on unit-unit distances for unit 812 out of 2042 . Time = 57\n",
      "working on unit-unit distances for unit 1223 out of 2042 . Time = 75\n",
      "working on unit-unit distances for unit 1627 out of 2042 . Time = 86\n",
      "working on unit-unit distances for unit 2037 out of 2042 . Time = 90\n",
      "All done; total elapsed  90 seconds for MD with 8 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT; SEE LATER FOR unit - unit ...\n",
    "print(\"Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default = too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "#populatedTractList = popHDlist.copy()  #nomenclature\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "64d7bf07-e51a-4a08-8931-592a3acbf1c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.386269851742003\n"
     ]
    }
   ],
   "source": [
    "print(maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bc91a02a-52b2-4ac3-afc2-7f1650e32922",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 2042 . Time = 0\n",
      "working on unit-unit angles for unit 200 out of 2042 . Time = 8\n",
      "working on unit-unit angles for unit 401 out of 2042 . Time = 16\n",
      "working on unit-unit angles for unit 601 out of 2042 . Time = 24\n",
      "working on unit-unit angles for unit 812 out of 2042 . Time = 30\n",
      "working on unit-unit angles for unit 1020 out of 2042 . Time = 34\n",
      "working on unit-unit angles for unit 1223 out of 2042 . Time = 39\n",
      "working on unit-unit angles for unit 1426 out of 2042 . Time = 42\n",
      "working on unit-unit angles for unit 1627 out of 2042 . Time = 44\n",
      "working on unit-unit angles for unit 1830 out of 2042 . Time = 46\n",
      "working on unit-unit angles for unit 2037 out of 2042 . Time = 46\n",
      "All done with angles; total elapsed seconds = 46\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "83cf363c-a1db-4c15-924a-df668c3507a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "772153.0 8 6177224.0 8.0 6177224.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4e4690c9-a317-4944-b5fa-8ba6af7c26e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6177224.0 6177224 1.0\n"
     ]
    }
   ],
   "source": [
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f5d44cb5-fd71-43ff-bc21-bdb7700fb8cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [2, 3, 12, 13, 15, 16, 19, 23]\n",
      "8 [3, 4, 18, 19, 21]\n",
      "15 [1, 3, 7, 12, 14]\n",
      "19 [1, 3, 4, 8, 16]\n",
      "[1, 8, 15, 19, 16, 17, 4, 18, 21, 7, 12, 14, 22]\n"
     ]
    }
   ],
   "source": [
    "for c in neighborCountyList[3]:\n",
    "    print(c,neighborCountyList[c])\n",
    "print(opt2nbrCtyList[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f6e5e17a-8256-4114-8b7d-518709494273",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3214fdc5-d104-4164-9478-7ab097ce01e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 2464.728 of targetWedgePop\n",
      "I am working on HD number 0 of 2042 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 401 of 2042 HDs.  Elapsed sec = 45\n",
      "I am working on HD number 812 of 2042 HDs.  Elapsed sec = 89\n",
      "I am working on HD number 1223 of 2042 HDs.  Elapsed sec = 137\n",
      "I am working on HD number 1627 of 2042 HDs.  Elapsed sec = 191\n",
      "I am working on HD number 2037 of 2042 HDs.  Elapsed sec = 250\n",
      "251.641 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\n",
      "unit avg and sd usage are 0.99958 0.11723\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y+zIoujd7J4koMDAQ9QNPZzp5sxq0nbsw5Ix9Ov7//G+t1+/dUV6H/VVnnD4aw2YTXgWi0z8MRu58AUsZFY0PD7ru3TEOj0Lq7/c4fJdzxiXgjb1VXt+WitGFKX729sLoSYjr7EC1qUdhqCtfZt95Oyi7v56f5wtfZxX2Ru8kEQUW3jLzM2/Go/TGuBxPCQDnrN6HKcD1INOi7097dZy4yHCHwcvdhSEAyD/U6PBd1ptb8BcfwhAA6KUIh3PyhL3O0xmCP+rdMSi+jG+xhzhPBcqtop4MivZ0LF5vDGgnooGBgciPfBmo2dNxOf7gepaW5zFu6JBQmM63Or2A+cuQsGCHC9ytyTEY2s04H2cXxjRjAja5mSFof9+kK2J7eNaOuo5vcbdcgD3EeRO6B/qtop4Oiu7ujxH7z10XRSWiwMZA5Ee+DtRMMyagcM1UbFs8EX98cDzuGW/o4zP1Tb25Ba9+clT+2ZvVkzUAnv/wq17uL/FOkMbx4pZ/qMFhzFVnAs4vjHeNNeBPc290+p7OF9SJVw7zqnfGG41nWzxeLsAevD0dWD/QbxX1xqBoV3+M6KXwQfXsPSK6hGOI/MiXgZpdB4RmjDXg6MlzfXWKPfZywVFcq49EmjEBE68Y5tEgb8D1s7386Zy1XfEYDHsvQ3eGDgnF9NF6xbYOm8Crn3yLLV84X9dH32UMyzOzR2PJDw/J7U3fnzqHP338rUPIrDdfWlhz0/wbcdfYH8N1mjEBU6+Lx8R1Hzsszmin+eH8/X2ryNuB0b01KLrzJAGuVE00uDEQ+ZG341FcDQi9/6bEPjm/3vLU9gpEakNhvtgGjZNel4Gs8Ogp+cJnE8Lt7bvmH9Ziuu2qS7e+civr8dT2im57ldJuiIcUESbPzrPfYlu67cteHWC9ubD7cVTZ277Eq9DgrrE/9nSEhQThd/ca8dgPAc3ZDVB/3yryZWB0bw6Ktk8SIKLBTSNEb/4KHnwsFgskSYLZbEZUVFSPjtVhE5jy4ifdzhqL0YXil3eNRtF3p/H/yk706POoZ4ZGhKL5ovueq6ERoVj/n2MAwKuenq4X9ec+qPRumYJe8rqT2z++hBB7L06DpQVnzlkRowuDXoqQe5N86WVx9Sw7+ztd3bpy9/81e09X4Zqp7O0hGqS8vX4zELnRm4EIcP0LngKbBoA0JLTbniFn7wF+vKgXfdeEeW8W98n5dWfokFD8ed5NmHjlMEU48OY2VXdrPum0wQgJ0ihunXoy9d0ealz10rkLNfb/rwHOe7o4DohocPP2+s1B1X6WZkzAoz9JdvuMMgosAo4LXnryHuDH2U7eTn/vLc0X2rBg836Hwdb2W0V3jx+OSV3CUmeuprfbnbd2OIwjczf1vcMm8NYXVT0aGM1B0UTkDY4h8rPcynqfFgykwanrStdzxiV0+5DVvuTLoo7dTW/vjn0xUGeLPHq7wnh3A6M5KJqIPMVA5Ee+Xjxo8Gs824IOm8AH/+6/J6jbQ8qzH3yFyPBQNJ61OowF6hokPH2YravP6xwGAd9uKbsbGM1B0UTkCQYiP+rJxYMGt2OnL/T4fx+ZE0fiH8U1PToPAaDBYsWCv+532Ods3E9vLNBoP4a3fzD4ugSAL882I6LBj4HIjwb66r7Uf7bsq8LImAj3hd24eVQM8g81osHSN/87q//hltrKO69BUuwQxEWGI/YybY+Pa+/h8SYQ+roEgK/PNiOiwY+ByI964+JBg1PzhTYUfnuqR8dY8W45IkL7dp6EAPBywRH5Z32UFkOHhMJ8oc3rW8Fde3i8+YOh6+KWnnB1O86XsVP9jb1cRL2PgcifOHiIuvFeWR2GhAXhQqvN52NcbPP9vb44abH26H/WnXt4PF1M8en065F1W7JXAcDds81cDfAeiNjLRdQ3OO3ej06ft/b3KdAA15Mw1B/sASM8xLtfJTG6UIceGU+fMO9tGAJ659lmA4GrJQ7cLWNg5+5Bv0Rqxh4iP4rV8ZYZDU4t7d4FuaczbnDozbA/Yf6xrWXQoHcfG9JbzzbrTz3t5XLVs/R0+mhE68J4+41Uj4HIn/g7hggAoI9yfnvMvphi1wu3L2OGOvP0dtyx0+d9Or4/eNPL1XWZAVfjp+rNLXj8HeXjZnj7jdSKgciPTp/jLTOiIA1gOt/qcn9fLKZ4a3IM9FHhbmfgbSupQfbUqwdUD4l9APW/3NwOs+vay+XtcgaBOMicqDcwEPmRp3+lEg1mNgE8/k4ZXg+6yWXw6e3FFIODNJh360jFDDlnGixWpz0s/cXbVbsBx98z3q5vFWiDzIl6CwORH92aHANdWBDOB9jAWaK+sOp//o1I7VdosPzYc9qXt2uSYod4VGfvienvsTTertrtaqFKX8ZFdXf7jWiwYiDyM07qILrkvLUD560dim2+3q7pvC5PrE4LaC7dou7c6+RpD+3fi6rx96Lqfh1L48uq3YDzQec9Wf9sIA8yJ+ptDER+VFJ1xu/rxBAFEl9u17i7rWQPNtNH66GP0ip6pLrTn2NpvL3N1e2g8x78Ecbb/KQmDER+1GC+2N+nQDTgdXe7pusKzabzVix958tur/n2YPPoT5K9Wh7AH2NpXK047WnPzE8njcIsY0K3t/d8Xf8swYfnxBEFMgYiPzrTzcwaIlL64ttTigu9s56gII37DhD7/r/srfL6HPpyLE13K0572jMzy5jg9rx86eXRwPc1n4gCFVeq9qPoIWH9fQpEAePVPd8h5YV8/LHgKHYfdL5Cs7/G5PX2WBp3K06bzls9WrXbkx4cdyuAdxU9xHEVcSI1YA+RH5kusIeIyBvNF9vwcsERh5Wr/a23xtJ02ASKv2/CU+9VdLvi9Au7vsbT6aOx9B3vVu12dQvO1QrgnQ2NCMX/uS1pwK3DROQvDER+FKNjDxGRL/orDLmayu4LT9cUst+mi9aFebVqd3e34KaP1mPlnVdjyxfH0HyxTd4/TBeGu8cbMH20vt+XGQgUrkInBT4GIj/ijA2iwNMbY2m8XVMIuHSb7u7xwz1atdvV8RvMLViytQxDh4Si+cKPQehSb1AysqdexYu5F7oLnbzFGPg4hsiPbIKLEBEFiiAN8OhPknt8ofN2TSE7+x9Q9lW77x4/HJOuHOb0Nll3D30FoAhDAGC+2IZXCo4g/1CDl2elXu7GfeV6+GgVGrgYiPxof1VTf58CEXlICOCNvVU9vtB5u6aQncnD6fK+HN8elJ7beQgdfTAyvcMmUPRdE/5ZfgJF3zX1yWf4kyehs6++S/If3jLzow72EBEFjN5ah8jXGWov7PoaM40Jbj/X1+P31ZICg/G2krvQyUedDA7sIfIj0znOMiMKJJ0vdL7ydexg58/trselp2MTe3NJgcF6W8nT74iPOgls7CHyo7IaU3+fAhH5oCcXOvs6QA3mFq/HETWebXHb49KT4wOXnnVW9F1Tj2dNubut1NerfvclT0MnJ84ENvYQ+ZHVi8cGENHA0ZMLnX0dIAAeL45od+z0Bbc9Lr4eX4NLizCu+v/KMe/NYqzIufSfU178xKeeHG9uKwUad4tberNQJv1ooI01YyDyo9a2DvdFRNRjUeG91/nt7ELn7S/yNGMCXnvoJuglz4KVBoA+SottJTUeDeR1dfyhQ0Ll43U9vgBgutDm8LBbX29vDebbSt2Fzu4WyiTXcivrMeXFT3oljPcW3jLzo9YO9hAR+YOlpb3XjvXgLSMVFzpnt7D0UVrMu3UkkmJ1ittOXRfx+2z1HSitNqHxbAuOnT6PlwuOOv1MAWDerSNd7rfXdB7Im2ZMcLpmUf6hBqeLO15s63CYjm8/ri+3twb7bSV76PR0oUxyrbt1sx7bWtZvj45hIPIjjYZ/PRAFmqTYIfJ/d/mL3GJVhJcEKRxzxiXgg3/XOx37c/f44W7/EjZfdAwrznzx7WlFAOo6y8lZULLZBBZs3u/ymL7MmnI3lqk3V/3uL65CJ3uGPDeQx5oxEPnR5ZeF4fR5z37JEdHAEPPDQ5m9WWCx3tyCv+ytcthu/wv4z/NvxAu7vnb5fg2Af5bXeXR+r+759sdz1YXh+dk3YFik1uGC3TnY/LP8hEfH9ub2VnfPTBtMt5W6fpfknYG8hAEDkR8Njx6Cr0+e7+/TICIvLNxSgsW3J+M/ro33aYHFzux/AT+1vaLb23oCQNP5VsToQmE63+bx7LEz51uRnfOlYpuzNYD66vYWbyuROwN5rJlXg6qTkpKg0WgcXkuXLgUAp/s0Gg1+//vfy8ewWq1YtmwZYmNjodPpMGfOHNTW1io+x2QyITMzE5IkQZIkZGZmorm5WVFTU1OD2bNnQ6fTITY2FsuXL0drq3Kdn4qKCqSmpiIiIgLDhw/H888/D9GPiyPeksy/KogCjU0Af9lbhb9+/l2vHE/A8zFO944fDsD72WmdORsk3RuzplwNLE8zJqBwzVRsWzwRf3xwPLYtnojCNVM9DkMDbeYR9a6BPNbMqx6iAwcOoKPjx5lSlZWVmD59Ou6//34AQH298p74v/71Lzz88MP4z//8T3nbypUrsXPnTuTk5GDYsGFYtWoVMjIyUFpaiuDgYADA/PnzUVtbi9zcXADAo48+iszMTOzcuRMA0NHRgfT0dFx++eUoLCxEU1MTFi5cCCEENm7cCACwWCyYPn067rjjDhw4cABHjhxBVlYWdDodVq1a5e331Cuujovsl88lop775JtTfv/MO0frcUtyjEOPizecjcvw9vZW18HhpvOteGGX67WRfL2tNBhXuSalgTzWTCN60GWycuVKfPjhhzh69KjTAcP33HMPzp49i48//hgAYDabcfnll+Mf//gH5s6dCwCoq6vDiBEjsHv3bsycORNff/01Ro8ejeLiYkyYMAEAUFxcjEmTJuHw4cO49tpr8a9//QsZGRk4fvw4DAYDACAnJwdZWVlobGxEVFQUXnvtNaxduxYnT56EVqsFAKxfvx4bN25EbW2txwOcLRYLJEmC2WxGVFSUr18VAOCJnC/xvofjAoho4IkMD8G5lnafFkD0hv2iULhmqsNstaMnz+LVPb71Vm1bPFERVDwJIM5qXJ0zAJ9nCLkasN7T49LAY/+3BpyH8d76t/b2+u3zOkStra3YunUrFi1a5DRcnDx5Ert27cLDDz8sbystLUVbWxtmzJghbzMYDDAajdi3bx8AoKioCJIkyWEIACZOnAhJkhQ1RqNRDkMAMHPmTFitVpSWlso1qampchiy19TV1eHYsWMu22W1WmGxWBSv3nLW2ntTgYkGot5c/2cgumnkUAA9u4XljrMems5PvL/tqst9PnbXcRnubm+5ehSHMz15yCkfnqourtbN0kvh/Rp8ff7ttWPHDjQ3NyMrK8vp/r/97W+IjIzEfffdJ29raGhAWFgYoqOjFbXx8fFoaGiQa+Li4hyOFxcXp6iJj49X7I+OjkZYWJiiJikpyeFz7PuSk5Odnve6devw3HPPuWh1z9hsXIeIBif7Rfy/UhLx318c689TcWvKVcNQ+G2TT+/9ydWXY96tI3t0C8sddwOQb02OQYwuDGfOe/9sRGfjMlzd3vJmVp2drzOEBvLMI+obA3EJA597iDZv3oxZs2Ypemk6++///m8sWLAA4eHuB0YJIRS9TM56nHqjxn53sLvbZWvXroXZbJZfx48fd3v+nnK2CBrRYGD/y276aH1/n4pbd1zr+AeXJ4I0QOakJLlX5Zd3Xd/LZwY8nX692wHIwUEa/OZuo1fH9eXREu5CSne8nSE0kGceUd/p3PM56cph/b4kg089RNXV1SgoKMD27dud7v/888/xzTff4N1331Vs1+v1aG1thclkUvQSNTY2YvLkyXLNyZMnHY556tQpuYdHr9dj/37lomImkwltbW2KGntvUefPAeDQu9SZVqtV3GbrTUEBvv4GUWcxulDcO3447hytV6zM3JMHjfa1BCkcmZOS8NfCKq8v9otvT0ZYyKW/IYODNIiL6r3fE/YxQ1m3JXt0UbhrbAJ+VpvsdK0jZ8cGvF8DqCfhw9sZQgN55hGph089RFu2bEFcXBzS09Od7t+8eTNSUlIwbtw4xfaUlBSEhoYiPz9f3lZfX4/Kyko5EE2aNAlmsxklJSVyzf79+2E2mxU1lZWVillteXl50Gq1SElJkWv27t2rmIqfl5cHg8HgcCvNXwLhr2cid4YOCcXbD0/AgV9Ox9Ozb1D8ZdeTB5n2NQ0uhYKwkCA8M3u0x+cXpAF+9pNkrL1rtGK7rxfn3noW1tq7RmPT/BsRowtVbO96CF/HZfjSPl8fcsqHp9JA4PUsM5vNhuTkZMybNw/r16932G+xWJCQkIA//OEPWLJkicP+xx57DB9++CHeeustxMTE4Oc//zmampoU0+5nzZqFuro6/OUvfwFwadr9qFGjFNPux48fj/j4ePz+97/HmTNnkJWVhXvuuUeedm82m3Httddi6tSp+MUvfoGjR48iKysLv/71r72adt+bs8xa22245lf/6tExiHw19drLezR13JsZIJ7OTPIXnTYYf7h/nOK8nZ1jjC4Uz6bfgFPnrag+cwGjYoYgc1KS3DPUWYdNYMqLn3jUG2bvAXo6/Xq8sOvrXp1W3nVKfMqoaPl5aT0Zl+FN+4Dem2UG9O3MI1IPb6/fXgeivLw8zJw5E9988w2uueYah/1vvPEGVq5cifr6ekiS5LC/paUFq1evxjvvvIOLFy9i2rRp2LRpE0aMGCHXnDlzBsuXL8cHH3wAAJgzZw5effVVDB06VK6pqanB448/jk8++QQRERGYP38+NmzYoLjdVVFRgaVLl6KkpATR0dFYsmQJfv3rX3v1TLHeDEQAsG73IY+6uYm688Sd12DksCF490ANir8/47be3sOx+2AdVr93EOetHW7fE6S5tCihnbcXbvuFuuBQA3L+97jbzxwaEYKWdhta2ryffPB/Jo/C4QYLir43KbZrAGSM1eOVB29yGgq6hglvw4Ori3jXcwB+vKD39DP9yZP22fXGekFch4h6U58HIrXp7UAEXApFb+yt8uivrs5P0Y7VaQENcPqcFVWnzuNvRcdg6jRQOyRIg/ZupqUO04Vh3AgJew6fGpDjO+yCAAQHa9DW0fdnqcGlZ8w1XWjz25RebUgQbkiIRFXTeZguOF+KIXpIKB64ORH/LK9Hg6X7i0Nruw3/KDqGz46ewv8eM+FC64/BI0YXit/cbcRdY3+c/NBhE3j1k6P4y97vFbUAcJk2GHNvHoE7R+t7raeh82du+eIYmjs9tLTrOCQAKP6+CUXfNQEQCA4KwrsHjiu+g866fh/278Jd705vctcbFugXdFch5en06xGtc3xmWk8FUmCkgY2BqJf1RSACfvzFfazp0rPNxo+IhmFohFcXIWe/OFrbbfjd7kM41nTpgjD9+ng0t7QpjtX1ojH3lpHYVlKNA8dMGBIahNEGCTG6MPy7thkCQPIwHeZPGIXy481osLSg0dKCQ3UWXGhtR3xUOMaPGArzxTYMjQhF88U2xFymRdxlWtiEwP6qM+iwCVhaWhGk0WBE9BAICJxobsGomCH4r5QR+P1Hh/Hv2mZEhYfi0SlXYMq1l9ZZKf6+Cfu+O42apgv4/tRZnLN2IDI8BJOvjEWsTovmi22oM1+EXtIieogWlpZLF9qhEWGIGRKKMxfacOa8FfXmFhikCERFhODregtONF/E8KER+K+UEZh8Vaw8GHjft6fx/0pr8XWDGUIA1+sjYRw+FJdHahF7mRaHG86i5syP/15xkVp8dcKMgsMnYbPZIKDBkLAQjBo2BCOjh6DO0oIR0UNwTdxlOFBtAiAw6YpYTPxhzI3936/B0oLTZ60wXbAiSBOESVcOw8QrhjksyOfJxcGb+g6bQPF3TSj6/jQAjeJz+4ovF7vO74m9TAsI4PR564C6WCrOsdMfLgPpHHuCIYUCEQNRL+urQERERER9x28rVRMRERENFgxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkel4FoqSkJGg0GofX0qVL5Zqvv/4ac+bMgSRJiIyMxMSJE1FTUyPvt1qtWLZsGWJjY6HT6TBnzhzU1tYqPsdkMiEzMxOSJEGSJGRmZqK5uVlRU1NTg9mzZ0On0yE2NhbLly9Ha2uroqaiogKpqamIiIjA8OHD8fzzz0MI4U2TiYiISAW8CkQHDhxAfX29/MrPzwcA3H///QCA7777DlOmTMF1112HTz/9FP/+97/x9NNPIzw8XD7GypUr8f777yMnJweFhYU4d+4cMjIy0NHRIdfMnz8f5eXlyM3NRW5uLsrLy5GZmSnv7+joQHp6Os6fP4/CwkLk5OTgvffew6pVq+Qai8WC6dOnw2Aw4MCBA9i4cSM2bNiAl156ybdvioiIiAYv0QMrVqwQV155pbDZbEIIIebOnSseeughl/XNzc0iNDRU5OTkyNtOnDghgoKCRG5urhBCiEOHDgkAori4WK4pKioSAMThw4eFEELs3r1bBAUFiRMnTsg127ZtE1qtVpjNZiGEEJs2bRKSJImWlha5Zt26dcJgMMjn6wmz2SwAyMclIiKigc/b67fPY4haW1uxdetWLFq0CBqNBjabDbt27cI111yDmTNnIi4uDhMmTMCOHTvk95SWlqKtrQ0zZsyQtxkMBhiNRuzbtw8AUFRUBEmSMGHCBLlm4sSJkCRJUWM0GmEwGOSamTNnwmq1orS0VK5JTU2FVqtV1NTV1eHYsWMu22W1WmGxWBQvIiIiGtx8DkQ7duxAc3MzsrKyAACNjY04d+4c1q9fj7S0NOTl5eHee+/Ffffdh88++wwA0NDQgLCwMERHRyuOFR8fj4aGBrkmLi7O4fPi4uIUNfHx8Yr90dHRCAsL67bG/rO9xpl169bJY5ckScKIESM8/UqIiIgoQPkciDZv3oxZs2bJvTQ2mw0AcPfdd+OJJ57A+PHj8dRTTyEjIwOvv/56t8cSQkCj0cg/d/7vvVkjfhhQ7ey9dmvXroXZbJZfx48f7/bciYiIKPD5FIiqq6tRUFCARx55RN4WGxuLkJAQjB49WlF7/fXXy7PM9Ho9WltbYTKZFDWNjY1y741er8fJkycdPvPUqVOKmq69PCaTCW1tbd3WNDY2AoBDz1FnWq0WUVFRihcRERENbj4Foi1btiAuLg7p6enytrCwMNxyyy345ptvFLVHjhzBqFGjAAApKSkIDQ2VZ6cBQH19PSorKzF58mQAwKRJk2A2m1FSUiLX7N+/H2azWVFTWVmJ+vp6uSYvLw9arRYpKSlyzd69exVT8fPy8mAwGJCUlORLs4mIiGiw8nbUdkdHhxg5cqRYs2aNw77t27eL0NBQ8cYbb4ijR4+KjRs3iuDgYPH555/LNUuWLBGJiYmioKBAlJWVialTp4px48aJ9vZ2uSYtLU2MHTtWFBUViaKiIjFmzBiRkZEh729vbxdGo1FMmzZNlJWViYKCApGYmCiys7PlmubmZhEfHy/mzZsnKioqxPbt20VUVJTYsGGDV+3lLDMiIqLA4+312+tA9NFHHwkA4ptvvnG6f/PmzeKqq64S4eHhYty4cWLHjh2K/RcvXhTZ2dkiJiZGREREiIyMDFFTU6OoaWpqEgsWLBCRkZEiMjJSLFiwQJhMJkVNdXW1SE9PFxERESImJkZkZ2crptgLIcTBgwfF7bffLrRardDr9eLZZ5/1asq9EAxEREREgcjb67dGCC7d3B2LxQJJkmA2mzmeiIiIKEB4e/3ms8yIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPUYiIiIiEj1GIiIiIhI9RiIiIiISPVC+vsEiIi80WETKKk6g8azLYiLDMetyTEIDtL092kRUYBjICKigJFbWY/ndh5CvblF3pYgheOZ2aORZkzoxzMjokDHW2ZEFBByK+vx2NYyRRgCgAZzCx7bWobcyvp+OjMiGgwYiIhowOuwCTy38xCEk332bc/tPIQOm7MKIiL3GIiIaMArqTrj0DPUmQBQb25BSdUZ/50UEQ0qDERENOA1nnUdhnypIyLqioGIiAa8uMjwXq0jIuqKgYiIBrxbk2OQIIXD1eR6DS7NNrs1Ocafp0VEgwgDERENeMFBGjwzezQAOIQi+8/PzB7N9YiIyGcMREQUENKMCXjtoZugl5S3xfRSOF576CauQ0REPcKFGYkoYKQZEzB9tJ4rVRNRr2MgIqKAEhykwaQrh/X3aRDRIMNbZkRERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHoMRERERKR6DERERESkegxEREREpHpeBaKkpCRoNBqH19KlSwEAWVlZDvsmTpyoOIbVasWyZcsQGxsLnU6HOXPmoLa2VlFjMpmQmZkJSZIgSRIyMzPR3NysqKmpqcHs2bOh0+kQGxuL5cuXo7W1VVFTUVGB1NRUREREYPjw4Xj++echhPCmyURERKQCId4UHzhwAB0dHfLPlZWVmD59Ou6//355W1paGrZs2SL/HBYWpjjGypUrsXPnTuTk5GDYsGFYtWoVMjIyUFpaiuDgYADA/PnzUVtbi9zcXADAo48+iszMTOzcuRMA0NHRgfT0dFx++eUoLCxEU1MTFi5cCCEENm7cCACwWCyYPn067rjjDhw4cABHjhxBVlYWdDodVq1a5U2ziYiIaLATPbBixQpx5ZVXCpvNJoQQYuHCheLuu+92Wd/c3CxCQ0NFTk6OvO3EiRMiKChI5ObmCiGEOHTokAAgiouL5ZqioiIBQBw+fFgIIcTu3btFUFCQOHHihFyzbds2odVqhdlsFkIIsWnTJiFJkmhpaZFr1q1bJwwGg3y+njCbzQKAfFwiIiIa+Ly9fvs8hqi1tRVbt27FokWLoNFo5O2ffvop4uLicM0112Dx4sVobGyU95WWlqKtrQ0zZsyQtxkMBhiNRuzbtw8AUFRUBEmSMGHCBLlm4sSJkCRJUWM0GmEwGOSamTNnwmq1orS0VK5JTU2FVqtV1NTV1eHYsWMu22W1WmGxWBQvIiIiGtx8DkQ7duxAc3MzsrKy5G2zZs3C22+/jU8++QR/+MMfcODAAUydOhVWqxUA0NDQgLCwMERHRyuOFR8fj4aGBrkmLi7O4fPi4uIUNfHx8Yr90dHRCAsL67bG/rO9xpl169bJY5ckScKIESM8+TqIiIgogHk1hqizzZs3Y9asWYpemrlz58r/3Wg04uabb8aoUaOwa9cu3HfffS6PJYRQ9DJ1/u+9WSN+GFDt7L12a9euxZNPPin/bLFYGIqIiIgGOZ96iKqrq1FQUIBHHnmk27qEhASMGjUKR48eBQDo9Xq0trbCZDIp6hobG+XeG71ej5MnTzoc69SpU4qarr08JpMJbW1t3dbYb9917TnqTKvVIioqSvEiIiKiwc2nQLRlyxbExcUhPT2927qmpiYcP34cCQkJAICUlBSEhoYiPz9frqmvr0dlZSUmT54MAJg0aRLMZjNKSkrkmv3798NsNitqKisrUV9fL9fk5eVBq9UiJSVFrtm7d69iKn5eXh4MBgOSkpJ8aTYRERENVt6O2u7o6BAjR44Ua9asUWw/e/asWLVqldi3b5+oqqoSe/bsEZMmTRLDhw8XFotFrluyZIlITEwUBQUFoqysTEydOlWMGzdOtLe3yzVpaWli7NixoqioSBQVFYkxY8aIjIwMeX97e7swGo1i2rRpoqysTBQUFIjExESRnZ0t1zQ3N4v4+Hgxb948UVFRIbZv3y6ioqLEhg0bvGovZ5kREREFHm+v314Hoo8++kgAEN98841i+4ULF8SMGTPE5ZdfLkJDQ8XIkSPFwoULRU1NjaLu4sWLIjs7W8TExIiIiAiRkZHhUNPU1CQWLFggIiMjRWRkpFiwYIEwmUyKmurqapGeni4iIiJETEyMyM7OVkyxF0KIgwcPittvv11otVqh1+vFs88+69WUeyEYiIiIiAKRt9dvjRBcurk7FosFkiTBbDZzPBEREVGA8Pb6zWeZERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRARERGR6nkViJKSkqDRaBxeS5cudaj92c9+Bo1Gg1deeUWx3Wq1YtmyZYiNjYVOp8OcOXNQW1urqDGZTMjMzIQkSZAkCZmZmWhublbU1NTUYPbs2dDpdIiNjcXy5cvR2tqqqKmoqEBqaioiIiIwfPhwPP/88xBCeNNkIiIiUgGvAtGBAwdQX18vv/Lz8wEA999/v6Jux44d2L9/PwwGg8MxVq5ciffffx85OTkoLCzEuXPnkJGRgY6ODrlm/vz5KC8vR25uLnJzc1FeXo7MzEx5f0dHB9LT03H+/HkUFhYiJycH7733HlatWiXXWCwWTJ8+HQaDAQcOHMDGjRuxYcMGvPTSS940mYiIiNRA9MCKFSvElVdeKWw2m7yttrZWDB8+XFRWVopRo0aJl19+Wd7X3NwsQkNDRU5OjrztxIkTIigoSOTm5gohhDh06JAAIIqLi+WaoqIiAUAcPnxYCCHE7t27RVBQkDhx4oRcs23bNqHVaoXZbBZCCLFp0yYhSZJoaWmRa9atWycMBoPifN0xm80CgHxcIiIiGvi8vX77PIaotbUVW7duxaJFi6DRaAAANpsNmZmZWL16NW644QaH95SWlqKtrQ0zZsyQtxkMBhiNRuzbtw8AUFRUBEmSMGHCBLlm4sSJkCRJUWM0GhU9UDNnzoTVakVpaalck5qaCq1Wq6ipq6vDsWPHXLbLarXCYrEoXkRERDS4+RyIduzYgebmZmRlZcnbXnzxRYSEhGD58uVO39PQ0ICwsDBER0crtsfHx6OhoUGuiYuLc3hvXFycoiY+Pl6xPzo6GmFhYd3W2H+21zizbt06eeySJEkYMWKEy1oiIiIaHHwORJs3b8asWbPkXprS0lL88Y9/xFtvvSX3GHlKCKF4j7P390aN+GFAdXfnt3btWpjNZvl1/PhxzxtCREREAcmnQFRdXY2CggI88sgj8rbPP/8cjY2NGDlyJEJCQhASEoLq6mqsWrUKSUlJAAC9Xo/W1laYTCbF8RobG+XeG71ej5MnTzp85qlTpxQ1XXt5TCYT2trauq1pbGwEAIeeo860Wi2ioqIULyIiIhrcfApEW7ZsQVxcHNLT0+VtmZmZOHjwIMrLy+WXwWDA6tWr8dFHHwEAUlJSEBoaKs9OA4D6+npUVlZi8uTJAIBJkybBbDajpKRErtm/fz/MZrOiprKyEvX19XJNXl4etFotUlJS5Jq9e/cqpuLn5eXBYDDIAY2IiIgIgPezzDo6OsTIkSPFmjVr3NZ2nWUmhBBLliwRiYmJoqCgQJSVlYmpU6eKcePGifb2drkmLS1NjB07VhQVFYmioiIxZswYkZGRIe9vb28XRqNRTJs2TZSVlYmCggKRmJgosrOz5Zrm5mYRHx8v5s2bJyoqKsT27dtFVFSU2LBhg1ft5SwzIiKiwNPns8wKCgpQU1ODRYsW+RTAXn75Zdxzzz144IEHcNttt2HIkCHYuXMngoOD5Zq3334bY8aMwYwZMzBjxgyMHTsW//jHP+T9wcHB2LVrF8LDw3HbbbfhgQcewD333IMNGzbINZIkIT8/H7W1tbj55pvx+OOP48knn8STTz7p03kTERHR4KURgks3d8disUCSJJjNZo4nIiIiChDeXr/5LDMiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj0GIiIiIlI9BiIiIiJSPQYiIiIiUj2vAlFSUhI0Go3Da+nSpQCAZ599Ftdddx10Oh2io6Nx5513Yv/+/YpjWK1WLFu2DLGxsdDpdJgzZw5qa2sVNSaTCZmZmZAkCZIkITMzE83NzYqampoazJ49GzqdDrGxsVi+fDlaW1sVNRUVFUhNTUVERASGDx+O559/HkIIb5pMREREKuBVIDpw4ADq6+vlV35+PgDg/vvvBwBcc801ePXVV1FRUYHCwkIkJSVhxowZOHXqlHyMlStX4v3330dOTg4KCwtx7tw5ZGRkoKOjQ66ZP38+ysvLkZubi9zcXJSXlyMzM1Pe39HRgfT0dJw/fx6FhYXIycnBe++9h1WrVsk1FosF06dPh8FgwIEDB7Bx40Zs2LABL730km/fFBEREQ1eogdWrFghrrzySmGz2ZzuN5vNAoAoKCgQQgjR3NwsQkNDRU5Ojlxz4sQJERQUJHJzc4UQQhw6dEgAEMXFxXJNUVGRACAOHz4shBBi9+7dIigoSJw4cUKu2bZtm9BqtcJsNgshhNi0aZOQJEm0tLTINevWrRMGg8Hl+XbXBvtxiYiIaODz9vrt8xii1tZWbN26FYsWLYJGo3G6/4033oAkSRg3bhwAoLS0FG1tbZgxY4ZcZzAYYDQasW/fPgBAUVERJEnChAkT5JqJEydCkiRFjdFohMFgkGtmzpwJq9WK0tJSuSY1NRVarVZRU1dXh2PHjvnabCIiIhqEQnx9444dO9Dc3IysrCzF9g8//BAPPvggLly4gISEBOTn5yM2NhYA0NDQgLCwMERHRyveEx8fj4aGBrkmLi7O4fPi4uIUNfHx8Yr90dHRCAsLU9QkJSU5fI59X3JystN2Wa1WWK1W+WeLxdLd10BERESDgM89RJs3b8asWbMUvTQAcMcdd6C8vBz79u1DWloaHnjgATQ2NnZ7LCGEopfJWY9Tb9SIHwZUO3uv3bp16+TB3JIkYcSIEd2eOxEREQU+nwJRdXU1CgoK8Mgjjzjs0+l0uOqqqzBx4kRs3rwZISEh2Lx5MwBAr9ejtbUVJpNJ8Z7Gxka590av1+PkyZMOxz116pSixt4TZGcymdDW1tZtjT2Yde1d6mzt2rUwm83y6/jx491+F0RERBT4fApEW7ZsQVxcHNLT093WCiHkW1ApKSkIDQ2VZ6cBQH19PSorKzF58mQAwKRJk2A2m1FSUiLX7N+/H2azWVFTWVmJ+vp6uSYvLw9arRYpKSlyzd69exVT8fPy8mAwGBxupXWm1WoRFRWleBEREdEg5+2o7Y6ODjFy5EixZs0axfZz586JtWvXiqKiInHs2DFRWloqHn74YaHVakVlZaVct2TJEpGYmCgKCgpEWVmZmDp1qhg3bpxob2+Xa9LS0sTYsWNFUVGRKCoqEmPGjBEZGRny/vb2dmE0GsW0adNEWVmZKCgoEImJiSI7O1uuaW5uFvHx8WLevHmioqJCbN++XURFRYkNGzZ41V7OMiMiIgo83l6/vQ5EH330kQAgvvnmG8X2ixcvinvvvVcYDAYRFhYmEhISxJw5c0RJSYlDXXZ2toiJiREREREiIyND1NTUKGqamprEggULRGRkpIiMjBQLFiwQJpNJUVNdXS3S09NFRESEiImJEdnZ2Yop9kIIcfDgQXH77bcLrVYr9Hq9ePbZZ72aci8EA9FA195hE/u+PS12fFkr9n17WrR3ePfvS0REg5O312+NEFy6uTsWiwWSJMFsNvP22QCTW1mP53YeQr25Rd6WIIXjmdmjkWZM6MczIyKi/ubt9ZvPMqOAlFtZj8e2linCEAA0mFvw2NYy5FbWu3gnERGRIwYiCjgdNoHndh6Cs65N+7bndh5Ch42dn0RE5BkGIgo4JVVnHHqGOhMA6s0tKKk647+TIiKigMZARAGn8azrMORLHREREQMRBZy4yPBerSMiImIgooBza3IMEqRwuHoAiwaXZpvdmhzjz9MiIqIAxkBEASc4SINnZo8GAIdQZP/5mdmjERzk+pl1REREnTEQUUBKMybgtYdugl5S3hbTS+F47aGbuA4RERF5JaS/T4DIV2nGBEwfrUdJ1Rk0nm1BXOSl22TsGSIiIm8xEFFACw7SYNKVw/r7NIiIKMDxlhkRERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeAxERERGpHgMRERERqR4DEREREakeV6p2QwgBALBYLP18JkREROQp+3Xbfh13h4HIjbNnzwIARowY0c9nQkRERN46e/YsJElyW6cRnkYnlbLZbKirq0NkZCQ0mt57aKjFYsGIESNw/PhxREVF9dpxBxK2MfAN9vYBbONgMNjbB7CNvhBC4OzZszAYDAgKcj9CiD1EbgQFBSExMbHPjh8VFTVo/8dtxzYGvsHePoBtHAwGe/sAttFbnvQM2XFQNREREakeAxERERGpHgNRP9FqtXjmmWeg1Wr7+1T6DNsY+AZ7+wC2cTAY7O0D2EZ/4KBqIiIiUj32EBEREZHqMRARERGR6jEQERERkeoxEBEREZHqMRD1k02bNiE5ORnh4eFISUnB559/3t+n5GDdunW45ZZbEBkZibi4ONxzzz345ptvFDVCCDz77LMwGAyIiIjAf/zHf+Crr75S1FitVixbtgyxsbHQ6XSYM2cOamtrFTUmkwmZmZmQJAmSJCEzMxPNzc193UQH69atg0ajwcqVK+Vtg6GNJ06cwEMPPYRhw4ZhyJAhGD9+PEpLS+X9gdzG9vZ2/OpXv0JycjIiIiJwxRVX4Pnnn4fNZgvY9u3duxezZ8+GwWCARqPBjh07FPv92Z6amhrMnj0bOp0OsbGxWL58OVpbW/u0jW1tbVizZg3GjBkDnU4Hg8GAn/70p6irqwuYNrr7N+zsZz/7GTQaDV555ZWAaZ+nbfz6668xZ84cSJKEyMhITJw4ETU1NQOzjYL8LicnR4SGhoo333xTHDp0SKxYsULodDpRXV3d36emMHPmTLFlyxZRWVkpysvLRXp6uhg5cqQ4d+6cXLN+/XoRGRkp3nvvPVFRUSHmzp0rEhIShMVikWuWLFkihg8fLvLz80VZWZm44447xLhx40R7e7tck5aWJoxGo9i3b5/Yt2+fMBqNIiMjw6/tLSkpEUlJSWLs2LFixYoV8vZAb+OZM2fEqFGjRFZWlti/f7+oqqoSBQUF4ttvvx0UbfzNb34jhg0bJj788ENRVVUl/ud//kdcdtll4pVXXgnY9u3evVv88pe/FO+9954AIN5//33Ffn+1p729XRiNRnHHHXeIsrIykZ+fLwwGg8jOzu7TNjY3N4s777xTvPvuu+Lw4cOiqKhITJgwQaSkpCiOMZDb6O7f0O79998X48aNEwaDQbz88ssB0z5P2vjtt9+KmJgYsXr1alFWVia+++478eGHH4qTJ08OyDYyEPWDW2+9VSxZskSx7brrrhNPPfVUP52RZxobGwUA8dlnnwkhhLDZbEKv14v169fLNS0tLUKSJPH6668LIS79YgsNDRU5OTlyzYkTJ0RQUJDIzc0VQghx6NAhAUAUFxfLNUVFRQKAOHz4sD+aJs6ePSuuvvpqkZ+fL1JTU+VANBjauGbNGjFlyhSX+wO9jenp6WLRokWKbffdd5946KGHBkX7ul5o/Nme3bt3i6CgIHHixAm5Ztu2bUKr1Qqz2dxnbXSmpKREAJD/cAykNrpqX21trRg+fLiorKwUo0aNUgSiQGqfqzbOnTtX/v+hMwOtjbxl5metra0oLS3FjBkzFNtnzJiBffv29dNZecZsNgMAYmJiAABVVVVoaGhQtEWr1SI1NVVuS2lpKdra2hQ1BoMBRqNRrikqKoIkSZgwYYJcM3HiREiS5LfvZOnSpUhPT8edd96p2D4Y2vjBBx/g5ptvxv3334+4uDjceOONePPNNwdNG6dMmYKPP/4YR44cAQD8+9//RmFhIe66665B0b6u/NmeoqIiGI1GGAwGuWbmzJmwWq2KW67+YDabodFoMHToUACB30abzYbMzEysXr0aN9xwg8P+wdC+Xbt24ZprrsHMmTMRFxeHCRMmKG6rDbQ2MhD52enTp9HR0YH4+HjF9vj4eDQ0NPTTWbknhMCTTz6JKVOmwGg0AoB8vt21paGhAWFhYYiOju62Ji4uzuEz4+Li/PKd5OTkoKysDOvWrXPYNxja+P333+O1117D1VdfjY8++ghLlizB8uXL8fe//10+N/v5dnf+A7WNa9aswbx583DdddchNDQUN954I1auXIl58+bJ52U/1+7OfaC2ryt/tqehocHhc6KjoxEWFubXNre0tOCpp57C/Pnz5Yd+BnobX3zxRYSEhGD58uVO9wd6+xobG3Hu3DmsX78eaWlpyMvLw7333ov77rsPn332mXxuA6mNfNp9P9FoNIqfhRAO2waS7OxsHDx4EIWFhQ77fGlL1xpn9f74To4fP44VK1YgLy8P4eHhLusCuY02mw0333wzfve73wEAbrzxRnz11Vd47bXX8NOf/tTl+QVKG999911s3boV77zzDm644QaUl5dj5cqVMBgMWLhwoctzC5T2ueKv9vR3m9va2vDggw/CZrNh06ZNbusDoY2lpaX44x//iLKyMq8/IxDaB0Ce1HD33XfjiSeeAACMHz8e+/btw+uvv47U1FSX7+2vNrKHyM9iY2MRHBzskFobGxsdEu5AsWzZMnzwwQfYs2cPEhMT5e16vR4Aum2LXq9Ha2srTCZTtzUnT550+NxTp071+XdSWlqKxsZGpKSkICQkBCEhIfjss8/wpz/9CSEhIfLnB3IbExISMHr0aMW266+/Xp7pEej/jqtXr8ZTTz2FBx98EGPGjEFmZiaeeOIJuccv0NvXlT/bo9frHT7HZDKhra3NL21ua2vDAw88gKqqKuTn58u9Q/ZzC9Q2fv7552hsbMTIkSPl3zvV1dVYtWoVkpKS5PMK1PYBl651ISEhbn/3DKQ2MhD5WVhYGFJSUpCfn6/Ynp+fj8mTJ/fTWTknhEB2dja2b9+OTz75BMnJyYr9ycnJ0Ov1ira0trbis88+k9uSkpKC0NBQRU19fT0qKyvlmkmTJsFsNqOkpESu2b9/P8xmc59/J9OmTUNFRQXKy8vl180334wFCxagvLwcV1xxRcC38bbbbnNYLuHIkSMYNWoUgMD/d7xw4QKCgpS/yoKDg+W/UAO9fV35sz2TJk1CZWUl6uvr5Zq8vDxotVqkpKT0aTvtYejo0aMoKCjAsGHDFPsDuY2ZmZk4ePCg4veOwWDA6tWr8dFHHwV8+4BL17pbbrml2989A66NHg+/pl5jn3a/efNmcejQIbFy5Uqh0+nEsWPH+vvUFB577DEhSZL49NNPRX19vfy6cOGCXLN+/XohSZLYvn27qKioEPPmzXM6/TcxMVEUFBSIsrIyMXXqVKfTKseOHSuKiopEUVGRGDNmjN+n3dt1nmUmROC3saSkRISEhIjf/va34ujRo+Ltt98WQ4YMEVu3bh0UbVy4cKEYPny4PO1++/btIjY2Vvzf//t/A7Z9Z8+eFV9++aX48ssvBQDx0ksviS+//FKeYeWv9tinM0+bNk2UlZWJgoICkZiY2CtTtrtrY1tbm5gzZ45ITEwU5eXlit8/Vqs1INro7t+wq66zzAZ6+zxp4/bt20VoaKh44403xNGjR8XGjRtFcHCw+PzzzwdkGxmI+smf//xnMWrUKBEWFiZuuukmeSr7QALA6WvLli1yjc1mE88884zQ6/VCq9WKn/zkJ6KiokJxnIsXL4rs7GwRExMjIiIiREZGhqipqVHUNDU1iQULFojIyEgRGRkpFixYIEwmkx9a6ahrIBoMbdy5c6cwGo1Cq9WK6667TrzxxhuK/YHcRovFIlasWCFGjhwpwsPDxRVXXCF++ctfKi6cgda+PXv2OP3/3sKFC/3enurqapGeni4iIiJETEyMyM7OFi0tLX3axqqqKpe/f/bs2RMQbXT3b9iVs0A0kNvnaRs3b94srrrqKhEeHi7GjRsnduzYMWDbqBFCCM/7k4iIiIgGH44hIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1WMgIiIiItVjICIiIiLVYyAiIiIi1fv/AWgVWIRLtcWQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"unit avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aec0cf55-112b-4eba-93c6-9cf1d1b87aae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_21Mar.csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b7a38aff-e4c4-498a-9f63-b6d5d02ab158",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "print(np.average(HDweight) * nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "253554da-8842-470e-b9b3-5626823059b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\n",
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.62785\n",
      "our final fractional capture area to normalize this cluster's usage is 0.26146\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.79483\n",
      "our final fractional capture area to normalize this cluster's usage is 0.22671\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip this block, use one below if I already CREATED HDVTDLIST'S FROM POLYGONS and there are non-vtd units but all tracts are vtds\n",
    "print(\"Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\")\n",
    "#origHDlist = [list() for t in range(nHDs)],\n",
    "splitTractNo, splitTractUse = [-999]*nHDs, [0.]*nHDs\n",
    "origHDpop = [0. for t in range(nHDs)]  #these will be based on captured Census vtd's\n",
    "HDpoly, HDdiam = [dummyPoly for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "\n",
    "popHDlist = list()  #rebuild here again with the geometries\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001 :  #HD poly was actually drawn  #[(HDradius[t][w]+0.001*maxD) for w in range(4)]\n",
    "        HDpoly[t] = buildPoly(hdCP[t], HDradius[t], HDangle[t])  #expand to catch the units on HD edge\n",
    "        HDdiam[t] = HDpoly[t].area **0.5  #approximate, for later scoring purposes of underuse * distance\n",
    "        popHDlist.append(t)\n",
    "\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t],0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]) )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t],1.3)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "868ec654-07ad-4925-bea5-105e8fe9bc59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDtractList = [list(set(oldHDtractList[t] ) ) for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6f48a4-26fe-46a0-84e0-61cf3e042780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3/10/24 - Georgia -- not sure why unitUse is off here when I pull from original HDtractList.  Used polygons instead, but still off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63418340-35f5-4a32-a0aa-6b04478b3f57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no 1 containing counties [22, 145, 40, 26]\n",
      "halfway there! last use was 0.9714\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.17306 from 202 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 2 containing counties [118, 67, 138, 126]\n",
      "halfway there! last use was 0.98332\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.3148 from 203 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n",
      "working on cluster no 3 containing counties [24]\n",
      "halfway there! last use was 0.95399\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.30829 from 238 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 2 in or out\n"
     ]
    }
   ],
   "source": [
    "#new for FL - alt to above, using pop-capture threshold on vtd Lists, not area threshold on HDpoly's for including CCBs\n",
    "oldHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j+1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in oldHDtractList[t]:\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e2b458c5-1240-49bc-9c8c-752d3604eaff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2157\n"
     ]
    }
   ],
   "source": [
    "print(len(origHDlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "beb8908f-1855-4224-9cbe-6f484a193f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping - is this from option 1??\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c1f835-ecad-444a-9f0e-0ab5c0e394e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4328e35e-089d-4f81-9987-a2cf0785a4ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\n",
      "Lets just pull in the HDtractLists from that HD2 code and use them\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2698 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "orig unit avg and sd usage are 0.94095 0.25629\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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bN09xcXGKjY3V/Pnz1a9fP40cObL5byEAAGhzAgqU1atXS5KGDx/ud/zZZ5/VPffcI0lasGCBampqNHPmTJWXl2vw4MHKy8tTVFSUb/3y5csVGhqqiRMnqqamRiNGjFB2drZCQkIu7NYAAIB24YLeByVYeB8UoPXxPigALlSrvQ8KAABASyBQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmhAZ7AADB12PhG8EeAQD8cAYFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGBOaLAHANCyeix8I9gjAEDAAj6DsmXLFt12221KSEiQx+PRa6+95ne9c04PP/ywEhISFBkZqeHDh2vPnj1+a2prazV79mx17dpVnTp10vjx43X48OELuiEAAKD9CDhQjh07pgEDBmjlypVnvX7p0qVatmyZVq5cqe3bt8vr9WrUqFGqqqryrcnIyFBubq5ycnK0detWVVdXa9y4caqvr2/6LQEAAO1GwA/xjBkzRmPGjDnrdc45rVixQosXL9Ydd9whSXruuecUHx+vF154QdOnT1dFRYXWrl2rDRs2aOTIkZKk559/XklJSXr77bc1evToC7g5AACgPWjWJ8kePHhQpaWlSk9P9x2LiIjQsGHDtG3bNklSYWGhTpw44bcmISFBKSkpvjVnqq2tVWVlpd8FAAC0X80aKKWlpZKk+Ph4v+Px8fG+60pLSxUeHq4uXbr84JozZWVlKSYmxndJSkpqzrEBAIAxLfIyY4/H4/e1c67BsTOda82iRYtUUVHhuxQXFzfbrAAAwJ5mDRSv1ytJDc6ElJWV+c6qeL1e1dXVqby8/AfXnCkiIkLR0dF+FwAA0H41a6D07NlTXq9X+fn5vmN1dXUqKChQWlqaJCk1NVVhYWF+a0pKSrR7927fGgAAcHEL+FU81dXV+uKLL3xfHzx4ULt27VJsbKyuvPJKZWRkKDMzU8nJyUpOTlZmZqY6duyoyZMnS5JiYmI0bdo0zZs3T3FxcYqNjdX8+fPVr18/36t6AADAxS3gQNmxY4duvfVW39dz586VJE2dOlXZ2dlasGCBampqNHPmTJWXl2vw4MHKy8tTVFSU73uWL1+u0NBQTZw4UTU1NRoxYoSys7MVEhLSDDcJAAC0dR7nnAv2EIGqrKxUTEyMKioqeD4KcB4X81vdH3p0bLBHAPA/Arn/5sMCAQCAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJgTGuwBgLakMZ8MzCfoAsCF4wwKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA4vMwaaGS9FBoALxxkUAABgDmdQgDasMWdrAKAt4gwKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHN4FQ9gFK/QAXAx4wwKAAAwhzMoQBBwdgQAzo0zKAAAwBwCBQAAmMNDPADaLT64EWi7OIMCAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDm/UhosCn30DAG0LgYI2j/gAgPaHh3gAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmMOreGAar9ABgIsTgYKgIT4AAD+Eh3gAAIA5nEFBi+DsCADgQnAGBQAAmEOgAAAAc3iIBwHj4RtcbBrzN3/o0bGtMAlw8SBQAKAZEDFA8yJQ4IezIwAACwgUABc1ohywiSfJAgAAcwgUAABgDoECAADM4TkoQdZcz/znFQSAffx3CjQegXIR4cmAAM5ENMGqoAbKqlWr9Nhjj6mkpETXXnutVqxYoZtvvjmYIwFAUFn8H4nWnIkzxjgtaIHyt7/9TRkZGVq1apWGDh2qv/zlLxozZoyKiop05ZVXBmsskyz+gwUALeFi/veO8PLncc65YPziwYMH67rrrtPq1at9x/r27asJEyYoKyvrnN9bWVmpmJgYVVRUKDo6uqVHbVEX83+MAGBdawbBxRAogdx/B+UMSl1dnQoLC7Vw4UK/4+np6dq2bVuD9bW1taqtrfV9XVFRIen7G9oSUh76f83yc3Y/Mvq8a07VHm+W3wUAaH5X/ualYI/gpzHzNOa+pzH3c435OYE6fb/dmHMjQQmUo0ePqr6+XvHx8X7H4+PjVVpa2mB9VlaWHnnkkQbHk5KSWmzG5hCzItgTAAAuNs1139OS92FVVVWKiYk555qgPknW4/H4fe2ca3BMkhYtWqS5c+f6vj516pS+/fZbxcXFnXV9MFVWViopKUnFxcVt/uEnK9jT5sV+Nj/2tHmxn83L0n4651RVVaWEhITzrg1KoHTt2lUhISENzpaUlZU1OKsiSREREYqIiPA7dumll7bkiBcsOjo66H8I7Q172rzYz+bHnjYv9rN5WdnP8505OS0o7yQbHh6u1NRU5efn+x3Pz89XWlpaMEYCAACGBO0hnrlz52rKlCkaNGiQhgwZojVr1uirr77SjBkzgjUSAAAwImiBMmnSJH3zzTf64x//qJKSEqWkpGjTpk3q3r17sEZqFhEREXrooYcaPCSFpmNPmxf72fzY0+bFfjavtrqfQXsfFAAAgB/CpxkDAABzCBQAAGAOgQIAAMwhUAAAgDkEShOsWrVKPXv2VIcOHZSamqr33nvvB9du3rxZHo+nweWzzz5rxYntC2RPpe8/n2nx4sXq3r27IiIi1Lt3b61bt66VprUvkP285557zvo3eu2117bixLYF+ve5ceNGDRgwQB07dlS3bt1077336ptvvmmladuGQPf06aefVt++fRUZGak+ffpo/fr1rTSpfVu2bNFtt92mhIQEeTwevfbaa+f9noKCAqWmpqpDhw7q1auX/vznP7f8oIFyCEhOTo4LCwtzzzzzjCsqKnJz5sxxnTp1cl9++eVZ17/77rtOktu7d68rKSnxXU6ePNnKk9sV6J4659z48ePd4MGDXX5+vjt48KD78MMP3fvvv9+KU9sV6H5+9913fn+bxcXFLjY21j300EOtO7hRge7ne++95y655BL35JNPugMHDrj33nvPXXvttW7ChAmtPLldge7pqlWrXFRUlMvJyXH79+93L774ouvcubN7/fXXW3lymzZt2uQWL17sXnnlFSfJ5ebmnnP9gQMHXMeOHd2cOXNcUVGRe+aZZ1xYWJh7+eWXW2fgRiJQAnTDDTe4GTNm+B27+uqr3cKFC8+6/nSglJeXt8J0bVOge/rmm2+6mJgY980337TGeG1OoPt5ptzcXOfxeNyhQ4daYrw2J9D9fOyxx1yvXr38jj311FMuMTGxxWZsawLd0yFDhrj58+f7HZszZ44bOnRoi83YVjUmUBYsWOCuvvpqv2PTp093N954YwtOFjge4glAXV2dCgsLlZ6e7nc8PT1d27ZtO+f3Dhw4UN26ddOIESP07rvvtuSYbUpT9vT111/XoEGDtHTpUl1xxRW66qqrNH/+fNXU1LTGyKZdyN/oaWvXrtXIkSPb/JsmNoem7GdaWpoOHz6sTZs2yTmnr7/+Wi+//LLGjh3bGiOb15Q9ra2tVYcOHfyORUZG6t///rdOnDjRYrO2V//6178a7P/o0aO1Y8cOU/tJoATg6NGjqq+vb/CBhvHx8Q0++PC0bt26ac2aNXrllVf06quvqk+fPhoxYoS2bNnSGiOb15Q9PXDggLZu3ardu3crNzdXK1as0Msvv6xZs2a1xsimNWU//1dJSYnefPNN3XfffS01YpvSlP1MS0vTxo0bNWnSJIWHh8vr9erSSy/Vn/70p9YY2bym7Ono0aP117/+VYWFhXLOaceOHVq3bp1OnDiho0ePtsbY7UppaelZ9//kyZOm9jNob3Xflnk8Hr+vnXMNjp3Wp08f9enTx/f1kCFDVFxcrMcff1y33HJLi87ZlgSyp6dOnZLH49HGjRt9n4q5bNky3XnnnXr66acVGRnZ4vNaF8h+/q/s7GxdeumlmjBhQgtN1jYFsp9FRUX69a9/rT/84Q8aPXq0SkpK9MADD2jGjBlau3Zta4zbJgSyp7///e9VWlqqG2+8Uc45xcfH65577tHSpUsVEhLSGuO2O2fb/7MdDybOoASga9euCgkJaVD5ZWVlDWr0XG688UZ9/vnnzT1em9SUPe3WrZuuuOIKv4/s7tu3r5xzOnz4cIvOa92F/I0657Ru3TpNmTJF4eHhLTlmm9GU/czKytLQoUP1wAMPqH///ho9erRWrVqldevWqaSkpDXGNq0pexoZGal169bp+PHjOnTokL766iv16NFDUVFR6tq1a2uM3a54vd6z7n9oaKji4uKCNFVDBEoAwsPDlZqaqvz8fL/j+fn5SktLa/TP2blzp7p169bc47VJTdnToUOH6siRI6qurvYd27dvny655BIlJia26LzWXcjfaEFBgb744gtNmzatJUdsU5qyn8ePH9cll/j/03r6//IdH312QX+jYWFhSkxMVEhIiHJycjRu3LgGe43zGzJkSIP9z8vL06BBgxQWFhakqc4iOM/NbbtOvzxu7dq1rqioyGVkZLhOnTr5XvGwcOFCN2XKFN/65cuXu9zcXLdv3z63e/dut3DhQifJvfLKK8G6CeYEuqdVVVUuMTHR3XnnnW7Pnj2uoKDAJScnu/vuuy9YN8GUQPfztLvvvtsNHjy4tcc1L9D9fPbZZ11oaKhbtWqV279/v9u6dasbNGiQu+GGG4J1E8wJdE/37t3rNmzY4Pbt2+c+/PBDN2nSJBcbG+sOHjwYpFtgS1VVldu5c6fbuXOnk+SWLVvmdu7c6XvZ9pn7efplxr/5zW9cUVGRW7t2LS8zbi+efvpp1717dxceHu6uu+46V1BQ4Ltu6tSpbtiwYb6vlyxZ4nr37u06dOjgunTp4m666Sb3xhtvBGFq2wLZU+ec+/TTT93IkSNdZGSkS0xMdHPnznXHjx9v5antCnQ/v/vuOxcZGenWrFnTypO2DYHu51NPPeWuueYaFxkZ6bp16+buuusud/jw4Vae2rZA9rSoqMj9+Mc/dpGRkS46Otrdfvvt7rPPPgvC1DadfjuLMy9Tp051zp39b3Tz5s1u4MCBLjw83PXo0cOtXr269Qc/D49znHMEAAC28OAdAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAz5YtW3TbbbcpISFBHo9Hr732WsA/wzmnxx9/XFdddZUiIiKUlJSkzMzMgH4Gn2YMAAB8jh07pgEDBujee+/Vz3/+8yb9jDlz5igvL0+PP/64+vXrp4qKCh09ejSgn8E7yQIAgLPyeDzKzc3VhAkTfMfq6ur0u9/9Ths3btR3332nlJQULVmyRMOHD5ckffrpp+rfv792796tPn36NPl38xAPAABotHvvvVfvv/++cnJy9PHHH+sXv/iFfvrTn+rzzz+XJP3jH/9Qr1699M9//lM9e/ZUjx49dN999+nbb78N6PcQKAAAoFH279+vF198US+99JJuvvlm9e7dW/Pnz9dNN92kZ599VpJ04MABffnll3rppZe0fv16ZWdnq7CwUHfeeWdAv4vnoAAAgEb56KOP5JzTVVdd5Xe8trZWcXFxkqRTp06ptrZW69ev961bu3atUlNTtXfv3kY/7EOgAACARjl16pRCQkJUWFiokJAQv+s6d+4sSerWrZtCQ0P9IqZv376SpK+++opAAQAAzWvgwIGqr69XWVmZbr755rOuGTp0qE6ePKn9+/erd+/ekqR9+/ZJkrp3797o38WreAAAgE91dbW++OILSd8HybJly3TrrbcqNjZWV155pe6++269//77euKJJzRw4EAdPXpU77zzjvr166ef/exnOnXqlK6//np17txZK1as0KlTpzRr1ixFR0crLy+v0XMQKAAAwGfz5s269dZbGxyfOnWqsrOzdeLECf3f//2f1q9fr//85z+Ki4vTkCFD9Mgjj6hfv36SpCNHjmj27NnKy8tTp06dNGbMGD3xxBOKjY1t9BwECgAAMIeXGQMAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOf8fdlzekSOwhggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.6927301840156793\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.9326155527100091\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.7581629715265193\n"
     ]
    }
   ],
   "source": [
    "#ALTERNATE RESTART FROM A DECENT HDtractList - this may not be robust for states with county clusters\n",
    "print(\"Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\")\n",
    "print(\"Lets just pull in the HDtractLists from that HD2 code and use them\")\n",
    "\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDtractList\"] #HDvtdList\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "# HDvPop = inDF[\"HDvPop\"].to_list()  #don't care; will rebuild from units\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()        \n",
    "\n",
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])\n",
    "\n",
    "origHDlist   = [HDunitList[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "origHDpop =     HDvPop.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "22916ca4-1217-4231-9f11-88110a1a7cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 263851.0 originally had use of 1.0575578630585878\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b99795f-d030-456d-9201-6519b739b472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\n",
      "this optional RESTART block pulls in an existing vtdlist for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv ./2024state_HD_output/FL7211needsEnclaveRemoval.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00435 0.16718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\")\n",
    "print(\"this optional RESTART block pulls in an existing vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "\n",
    "#infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv\")\n",
    "#inDF = pd.read_csv(infile)\n",
    "#vtdListString = inDF[\"HDvtdList\"]\n",
    "#nHDs = len(vtdListString)\n",
    "#inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "#HDweight = inDF[\"HDweight\"]\n",
    "#HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "#hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "#hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "#print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 1994 populated HDs\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9psbGRrvGsiwtWrRIXq9XnTp10qhRo3Tw4MGA8/j9fs2cOVPdunVTVFSU0tPTVVZWFup2AQCAoUIeYpYsWaLf//73WrVqlQ4fPqylS5fqiSee0NNPP23XLF26VMuXL9eqVau0Z88eeTwejRs3TtXV1XZNVlaW8vPzlZeXpx07dqimpkZpaWlqaGgIdcsAAMBAzlCfcNeuXfrXf/1XTZgwQZLUp08fbdy4UXv37pX03SrMypUr9fDDD+tHP/qRJOn555+X2+3Whg0bNH36dPl8Pq1du1br1q3T2LFjJUkvvvii4uPj9cYbb2j8+PGhbhsAABgm5Csxt9xyi/7yl7/oyJEjkqT//d//1Y4dO3T77bdLkkpKSlReXq7U1FT7mMjISI0cOVI7d+6UJBUVFam+vj6gxuv1KjEx0a45k9/vV1VVVcAGAADar5CvxMyfP18+n0/XXHONwsLC1NDQoN/+9rf6t3/7N0lSeXm5JMntdgcc53a7dfToUbsmIiJCXbt2bVLz/fFnysnJ0eLFi0N9OQAAoI0K+UrMpk2b9OKLL2rDhg3at2+fnn/+eT355JN6/vnnA+ocDkfAa8uymoyd6Xw1CxculM/ns7fS0tJ/7kIAAECbFvKVmIceekgLFizQT3/6U0lSUlKSjh49qpycHE2dOlUej0fSd6stPXr0sI+rqKiwV2c8Ho/q6upUWVkZsBpTUVGhlJSUs75vZGSkIiMjQ305AACgjQr5Sszp06d12WWBpw0LC7N/Yt23b195PB4VFBTY++vq6lRYWGgHlOTkZIWHhwfUHD9+XAcOHDhniAEAAB1LyFdiJk6cqN/+9rfq1auXrr32Wu3fv1/Lly/Xv//7v0v67mukrKwsZWdnKyEhQQkJCcrOzlbnzp01ZcoUSZLL5dK0adM0Z84cxcbGKiYmRnPnzlVSUpL9ayUAANCxhTzEPP300/rVr36lGTNmqKKiQl6vV9OnT9evf/1ru2bevHmqra3VjBkzVFlZqaFDh2rbtm3q0qWLXbNixQo5nU5lZGSotrZWY8aMUW5ursLCwkLdMgAAMJDDsiyrtZtoDlVVVXK5XPL5fIqOjm7tdoA2rc+C11q7haB99viE1m4BQDMI5vObv50EAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARmqWEPPFF1/oZz/7mWJjY9W5c2ddf/31KioqsvdblqVFixbJ6/WqU6dOGjVqlA4ePBhwDr/fr5kzZ6pbt26KiopSenq6ysrKmqNdAABgoJCHmMrKSg0fPlzh4eH685//rEOHDmnZsmW64oor7JqlS5dq+fLlWrVqlfbs2SOPx6Nx48apurrarsnKylJ+fr7y8vK0Y8cO1dTUKC0tTQ0NDaFuGQAAGMhhWZYVyhMuWLBAb7/9tt56662z7rcsS16vV1lZWZo/f76k71Zd3G63lixZounTp8vn86l79+5at26dJk+eLEk6duyY4uPjtWXLFo0fP/6CfVRVVcnlcsnn8yk6Ojp0Fwi0Q30WvNbaLQTts8cntHYLAJpBMJ/fIV+JeeWVVzRkyBD95Cc/UVxcnAYPHqw//OEP9v6SkhKVl5crNTXVHouMjNTIkSO1c+dOSVJRUZHq6+sDarxerxITE+2aM/n9flVVVQVsAACg/Qp5iPn000+1Zs0aJSQk6PXXX9f999+vWbNm6YUXXpAklZeXS5LcbnfAcW63295XXl6uiIgIde3a9Zw1Z8rJyZHL5bK3+Pj4UF8aAABoQ0IeYhobG3XDDTcoOztbgwcP1vTp03XvvfdqzZo1AXUOhyPgtWVZTcbOdL6ahQsXyufz2Vtpaek/dyEAAKBNC3mI6dGjhwYOHBgwNmDAAH3++eeSJI/HI0lNVlQqKirs1RmPx6O6ujpVVlaes+ZMkZGRio6ODtgAAED7FfIQM3z4cH344YcBY0eOHFHv3r0lSX379pXH41FBQYG9v66uToWFhUpJSZEkJScnKzw8PKDm+PHjOnDggF0DAAA6NmeoT/jLX/5SKSkpys7OVkZGht599109++yzevbZZyV99zVSVlaWsrOzlZCQoISEBGVnZ6tz586aMmWKJMnlcmnatGmaM2eOYmNjFRMTo7lz5yopKUljx44NdcsAAMBAIQ8xN954o/Lz87Vw4UI99thj6tu3r1auXKk777zTrpk3b55qa2s1Y8YMVVZWaujQodq2bZu6dOli16xYsUJOp1MZGRmqra3VmDFjlJubq7CwsFC3DAAADBTy58S0FTwnBrh4PCcGQFvRqs+JAQAAaAmEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIzR5icnJy5HA4lJWVZY9ZlqVFixbJ6/WqU6dOGjVqlA4ePBhwnN/v18yZM9WtWzdFRUUpPT1dZWVlzd0uAAAwRLOGmD179ujZZ5/VddddFzC+dOlSLV++XKtWrdKePXvk8Xg0btw4VVdX2zVZWVnKz89XXl6eduzYoZqaGqWlpamhoaE5WwYAAIZothBTU1OjO++8U3/4wx/UtWtXe9yyLK1cuVIPP/ywfvSjHykxMVHPP/+8Tp8+rQ0bNkiSfD6f1q5dq2XLlmns2LEaPHiwXnzxRb3//vt64403mqtlAABgkGYLMQ888IAmTJigsWPHBoyXlJSovLxcqamp9lhkZKRGjhypnTt3SpKKiopUX18fUOP1epWYmGjXnMnv96uqqipgAwAA7ZezOU6al5enffv2ac+ePU32lZeXS5LcbnfAuNvt1tGjR+2aiIiIgBWc72u+P/5MOTk5Wrx4cSjaBwAABgj5Skxpaal+8Ytf6MUXX9Tll19+zjqHwxHw2rKsJmNnOl/NwoUL5fP57K20tDT45gEAgDFCHmKKiopUUVGh5ORkOZ1OOZ1OFRYW6qmnnpLT6bRXYM5cUamoqLD3eTwe1dXVqbKy8pw1Z4qMjFR0dHTABgAA2q+Qh5gxY8bo/fffV3Fxsb0NGTJEd955p4qLi9WvXz95PB4VFBTYx9TV1amwsFApKSmSpOTkZIWHhwfUHD9+XAcOHLBrAABAxxbye2K6dOmixMTEgLGoqCjFxsba41lZWcrOzlZCQoISEhKUnZ2tzp07a8qUKZIkl8uladOmac6cOYqNjVVMTIzmzp2rpKSkJjcKAwCAjqlZbuy9kHnz5qm2tlYzZsxQZWWlhg4dqm3btqlLly52zYoVK+R0OpWRkaHa2lqNGTNGubm5CgsLa42WAQBAG+OwLMtq7SaaQ1VVlVwul3w+H/fHABfQZ8Frrd1C0D57fEJrtwCgGQTz+c3fTgIAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjhTzE5OTk6MYbb1SXLl0UFxenSZMm6cMPPwyosSxLixYtktfrVadOnTRq1CgdPHgwoMbv92vmzJnq1q2boqKilJ6errKyslC3CwAADBXyEFNYWKgHHnhAu3fvVkFBgb799lulpqbq1KlTds3SpUu1fPlyrVq1Snv27JHH49G4ceNUXV1t12RlZSk/P195eXnasWOHampqlJaWpoaGhlC3DAAADOSwLMtqzjf46quvFBcXp8LCQv3Lv/yLLMuS1+tVVlaW5s+fL+m7VRe3260lS5Zo+vTp8vl86t69u9atW6fJkydLko4dO6b4+Hht2bJF48ePv+D7VlVVyeVyyefzKTo6ujkvETBenwWvtXYLHcJnj09o7RaANi+Yz+9mvyfG5/NJkmJiYiRJJSUlKi8vV2pqql0TGRmpkSNHaufOnZKkoqIi1dfXB9R4vV4lJibaNWfy+/2qqqoK2AAAQPvVrCHGsizNnj1bt9xyixITEyVJ5eXlkiS32x1Q63a77X3l5eWKiIhQ165dz1lzppycHLlcLnuLj48P9eUAAIA2pFlDzIMPPqj33ntPGzdubLLP4XAEvLYsq8nYmc5Xs3DhQvl8PnsrLS299MYBAECb12whZubMmXrllVf05ptvqmfPnva4x+ORpCYrKhUVFfbqjMfjUV1dnSorK89Zc6bIyEhFR0cHbAAAoP0KeYixLEsPPvigXn75Zf31r39V3759A/b37dtXHo9HBQUF9lhdXZ0KCwuVkpIiSUpOTlZ4eHhAzfHjx3XgwAG7BgAAdGzOUJ/wgQce0IYNG/Q///M/6tKli73i4nK51KlTJzkcDmVlZSk7O1sJCQlKSEhQdna2OnfurClTpti106ZN05w5cxQbG6uYmBjNnTtXSUlJGjt2bKhbBgAABgp5iFmzZo0kadSoUQHjzz33nH7+859LkubNm6fa2lrNmDFDlZWVGjp0qLZt26YuXbrY9StWrJDT6VRGRoZqa2s1ZswY5ebmKiwsLNQtAwAAAzX7c2JaC8+JAS4ez4lpGTwnBriwNvWcGAAAgOZAiAEAAEYixAAAACOF/MZeoKPj/hIAaBmsxAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYydnaDQBAR9FnwWut3ULQPnt8Qmu3AJwTKzEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACPxnBgAwDnxbBu0ZazEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMxHNi0KaZ+IwKAEDLYCUGAAAYiRADAACMxNdJAIB2xdSvoflzCcFjJQYAABiJEAMAAIxEiAEAAEZq8/fErF69Wk888YSOHz+ua6+9VitXrtSIESNauy0jmfo9MQAAZ9OmQ8ymTZuUlZWl1atXa/jw4XrmmWd022236dChQ+rVq1er9kYgAACgdTksy7Jau4lzGTp0qG644QatWbPGHhswYIAmTZqknJyc8x5bVVUll8sln8+n6OjokPdGiAEAdHTN8YuqYD6/2+xKTF1dnYqKirRgwYKA8dTUVO3cubNJvd/vl9/vt1/7fD5J3/2X0Rwa/aeb5bwAAJiiOT5jvz/nxayxtNkQc+LECTU0NMjtdgeMu91ulZeXN6nPycnR4sWLm4zHx8c3W48AAHRkrpXNd+7q6mq5XK7z1rTZEPM9h8MR8NqyrCZjkrRw4ULNnj3bft3Y2KhvvvlGsbGxZ63HuVVVVSk+Pl6lpaXN8lUcLg7z0PqYg7aBeWgbWmoeLMtSdXW1vF7vBWvbbIjp1q2bwsLCmqy6VFRUNFmdkaTIyEhFRkYGjF1xxRXN2WK7Fx0dzf9htAHMQ+tjDtoG5qFtaIl5uNAKzPfa7HNiIiIilJycrIKCgoDxgoICpaSktFJXAACgrWizKzGSNHv2bGVmZmrIkCEaNmyYnn32WX3++ee6//77W7s1AADQytp0iJk8ebK+/vprPfbYYzp+/LgSExO1ZcsW9e7du7Vba9ciIyP16KOPNvl6Di2LeWh9zEHbwDy0DW1xHtr0c2IAAADOpc3eEwMAAHA+hBgAAGAkQgwAADASIQYAABiJENNBrV69Wn379tXll1+u5ORkvfXWWxd13Ntvvy2n06nrr7++eRvsIIKdB7/fr4cffli9e/dWZGSkrrrqKv3Xf/1XC3XbPgU7B+vXr9egQYPUuXNn9ejRQ3fffbe+/vrrFuq2fdq+fbsmTpwor9crh8OhzZs3X/CYwsJCJScn6/LLL1e/fv30+9//vvkbbceCnYOXX35Z48aNU/fu3RUdHa1hw4bp9ddfb5lm/wEhpgPatGmTsrKy9PDDD2v//v0aMWKEbrvtNn3++efnPc7n8+muu+7SmDFjWqjT9u1S5iEjI0N/+ctftHbtWn344YfauHGjrrnmmhbsun0Jdg527Nihu+66S9OmTdPBgwf10ksvac+ePbrnnntauPP25dSpUxo0aJBWrVp1UfUlJSW6/fbbNWLECO3fv1//8R//oVmzZum///u/m7nT9ivYOdi+fbvGjRunLVu2qKioSKNHj9bEiRO1f//+Zu70DBY6nJtuusm6//77A8auueYaa8GCBec9bvLkydYjjzxiPfroo9agQYOascOOIdh5+POf/2y5XC7r66+/bon2OoRg5+CJJ56w+vXrFzD21FNPWT179my2HjsaSVZ+fv55a+bNm2ddc801AWPTp0+3br755mbsrOO4mDk4m4EDB1qLFy8OfUPnwUpMB1NXV6eioiKlpqYGjKempmrnzp3nPO65557TJ598okcffbS5W+wQLmUeXnnlFQ0ZMkRLly7VlVdeqauvvlpz585VbW1tS7Tc7lzKHKSkpKisrExbtmyRZVn68ssv9cc//lETJkxoiZbxf3bt2tVk3saPH6+9e/eqvr6+lbrq2BobG1VdXa2YmJgWfd82/cRehN6JEyfU0NDQ5I9out3uJn9s83sfffSRFixYoLfeektOJ/+TCYVLmYdPP/1UO3bs0OWXX678/HydOHFCM2bM0DfffMN9MZfgUuYgJSVF69ev1+TJk/X3v/9d3377rdLT0/X000+3RMv4P+Xl5Wedt2+//VYnTpxQjx49WqmzjmvZsmU6deqUMjIyWvR9WYnpoBwOR8Bry7KajElSQ0ODpkyZosWLF+vqq69uqfY6jIudB+m7f+k4HA6tX79eN910k26//XYtX75cubm5rMb8E4KZg0OHDmnWrFn69a9/raKiIm3dulUlJSX8PbdWcLZ5O9s4mt/GjRu1aNEibdq0SXFxcS363vyzuoPp1q2bwsLCmvxLs6Kiosm/bCSpurpae/fu1f79+/Xggw9K+u7D1LIsOZ1Obdu2TT/84Q9bpPf2JNh5kKQePXroyiuvDPgT9QMGDJBlWSorK1NCQkKz9tzeXMoc5OTkaPjw4XrooYckSdddd52ioqI0YsQI/eY3v2EFoIV4PJ6zzpvT6VRsbGwrddUxbdq0SdOmTdNLL72ksWPHtvj7sxLTwURERCg5OVkFBQUB4wUFBUpJSWlSHx0drffff1/FxcX2dv/996t///4qLi7W0KFDW6r1diXYeZCk4cOH69ixY6qpqbHHjhw5ossuu0w9e/Zs1n7bo0uZg9OnT+uyywL/bzMsLEzS/18JQPMbNmxYk3nbtm2bhgwZovDw8FbqquPZuHGjfv7zn2vDhg2td19Yi95GjDYhLy/PCg8Pt9auXWsdOnTIysrKsqKioqzPPvvMsizLWrBggZWZmXnO4/l1UmgEOw/V1dVWz549rTvuuMM6ePCgVVhYaCUkJFj33HNPa12C8YKdg+eee85yOp3W6tWrrU8++cTasWOHNWTIEOumm25qrUtoF6qrq639+/db+/fvtyRZy5cvt/bv328dPXrUsqym8/Dpp59anTt3tn75y19ahw4dstauXWuFh4dbf/zjH1vrEowX7Bxs2LDBcjqd1u9+9zvr+PHj9nby5MkW7ZsQ00H97ne/s3r37m1FRERYN9xwg1VYWGjvmzp1qjVy5MhzHkuICZ1g5+Hw4cPW2LFjrU6dOlk9e/a0Zs+ebZ0+fbqFu25fgp2Dp556yho4cKDVqVMnq0ePHtadd95plZWVtXDX7cubb75pSWqyTZ061bKss8/D3/72N2vw4MFWRESE1adPH2vNmjUt33g7EuwcjBw58rz1LcVhWayBAgAA83BPDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAAAQlO3bt2vixInyer1yOBzavHlz0OewLEtPPvmkrr76akVGRio+Pl7Z2dlBnYM/AAkAAIJy6tQpDRo0SHfffbd+/OMfX9I5fvGLX2jbtm168sknlZSUJJ/PpxMnTgR1Dp7YCwAALpnD4VB+fr4mTZpkj9XV1emRRx7R+vXrdfLkSSUmJmrJkiUaNWqUJOnw4cO67rrrdODAAfXv3/+S35uvkwAAQEjdfffdevvtt5WXl6f33ntPP/nJT3Trrbfqo48+kiS9+uqr6tevn/70pz+pb9++6tOnj+655x598803Qb0PIQYAAITMJ598oo0bN+qll17SiBEjdNVVV2nu3Lm65ZZb9Nxzz0mSPv30Ux09elQvvfSSXnjhBeXm5qqoqEh33HFHUO/FPTEAACBk9u3bJ8uydPXVVweM+/1+xcbGSpIaGxvl9/v1wgsv2HVr165VcnKyPvzww4v+iokQAwAAQqaxsVFhYWEqKipSWFhYwL4f/OAHkqQePXrI6XQGBJ0BAwZIkj7//HNCDAAAaHmDBw9WQ0ODKioqNGLEiLPWDB8+XN9++60++eQTXXXVVZKkI0eOSJJ69+590e/Fr5MAAEBQampq9PHHH0v6LrQsX75co0ePVkxMjHr16qWf/exnevvtt7Vs2TINHjxYJ06c0F//+lclJSXp9ttvV2Njo2688Ub94Ac/0MqVK9XY2KgHHnhA0dHR2rZt20X3QYgBAABB+dvf/qbRo0c3GZ86dapyc3NVX1+v3/zmN3rhhRf0xRdfKDY2VsOGDdPixYuVlJQkSTp27Jhmzpypbdu2KSoqSrfddpuWLVummJiYi+6DEAMAAIzET6wBAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMNL/A0x72ed6/MiPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 705 time is now 12\n",
      "working on HD 1433 time is now 19\n",
      "out of 1994 total HDs, there were 1664.0 contiguous and 1756.0 complement-contiguous HDs\n",
      "42 HDs had both discontiguity problems, while enclave-only = 196 and discontig only= 288\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 330 196\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE - continue here for option 1 or option 2 drawing method\n",
    "# 3/4/24 - for FL, using 6 or unspecified loops now yields same results\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,6) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ab9f20-ddb8-47b1-9e5d-5cfda17b2b25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 733545 district pop vs 772153 target\n",
      "we'll also trim any HD with total islands' pop <= 15443\n",
      "working on HD 0\n",
      "working on HD 1559\n",
      "Out of 330 discontig HDs 330 had discontig HDs.\n",
      "Of these, 180 won't be under 733545 after all minor discontigys were shed, while 150 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 0\n",
      "There are 150 HDs still with both discontinuity problems\n",
      "Additionally, 19 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 2\n",
      "Out of 215 HDpolys that generated enclaves, 0 had contiguous adjrs, while 193 neighbored a state boundary while 22 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 810760 district pop vs 772153 target\n",
      "working on enclave-y HD 63 . We have evaluated 0 of 215 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 823 . We have evaluated 100 of 215 enclavy HDs.Time is now 5\n",
      "working on enclave-y HD 650 . We have evaluated 200 of 215 enclavy HDs.Time is now 10\n",
      "Out of 215 HDs with enclaves 215 had enclaves.\n",
      "Of these, 191 wouldn't be over 810760 if all enclaves filled, while 24 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 193 42 18\n",
      "24 24 191\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.94494 0.09401\n",
      "CCB cluster 0 = unit 1646 with pop 251617.0 now has use of 0.8996701430933163\n",
      "CCB cluster 1 = unit 1647 with pop 103725.0 now has use of 1.0676355592737798\n",
      "CCB cluster 2 = unit 1648 with pop 373177.0 now has use of 0.9912867009516644\n",
      "CCB cluster 3 = unit 1649 with pop 284018.0 now has use of 0.999720262693957\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7729308-3cd8-4c12-9199-f400f59e6f2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 24 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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ltCWB6MyBeD0Ni7jqCgff/Ws7AEqNgq4DQknsEcz/Zg8C4LBFyb+++bPxeZ8FyJaazo8c/dRWyN+D8w5nngXLujwAHAcrUJjU6HofTx4papSI4UpU4Qb8LojBmlZC1apsit/bTfgDA1AGnNtJvkJSu5JdWIHn13Iqfk4jpJcvYsZSVsqifzyCo7oa8KNblyDQu+CzKyB1Jlz8PBhCWnSsiISeeCUBq61+a5jD7eGjdZlM6RNBkEENQGJAIl8f+Bq7245Wqa23X0MIgoDkafxLn7F+DyvW/U6sJoybHrmz1lIlebyYMirJDlSRpKr7E6xWqPlXcjhX7Zd4fe8y/tbnktp9moihvv7OJkSx2uD7625g+WnT/2D/j77n6/8DKVN9zx1myFyDfvRdkLMUq8uKQWUgeASUroM3X/2a+x68EsVJfkhWs5MFT2/E6/Fy47yRGAOPf657RJr47JYh3PD+Jv69qYI+cfuZOKh74/M/g8iipvMjW2pkZFpJ+dcHsKWV4CqoRhmqI+j6HgiK+r9SglLE0D+MsDtSETUiRf/ehmVj86rXd1ZEUSRieE8UghKFTl27feUH81EoVcz8xwtMvf9hBj6+CG78Aaa9BRnL4T+D4PCqZh/HZrWy6WOfA21C76H1tlmwMYv8Shv3jkuq3ZYUkIRbcnO48nDLz00hQhO+4D+tWIZeoeHy2VcdFzReibIvDxBR4uDHXoZ6+42K6s80Yyb/LgllS/6W2u3Or3zh2freNzd+YEWNqPA0YNHxukFx/P9B+lLfI3MNBHfDv/tlAJTZywC4ZvY4jJMrURwK4p3PvvadhySxd20eW37KZNXn+3Ha3Fw4q0cdQVN7Pt1CCdT7fHHuWZzBwZzCxud/BpEjoDo/sqhpS+Tvw3mFwqRBNKoIu7Mvobf0QRPfdG0xUa8ieHYvVNF+VHx7ENv+sg6Y6ZnjwEe/ohBVhIz2ZWitKMjn4OYNjLnhZmJ69Kb7iAtQqTU+L/v+18O9WyAkBX5+xJdUrwm2//YVupciuSD3XdZHzya2z6m+NBaHm//8dpCrBsaSFOZXu71PSB+ijdE8svoRSmwlLTovhVoBjfh/Z+7MoNxbxZhhowmKDgV8QqBiyUFsu4pZekEIe8IattS90n8KEWIld6cXYbZXAODU63ApFegjGs6/A/gyLUPDosZuhtAUeLwQLv8f3LEG/lHpe9y3hciArgAUWo+LjxunXY7UpwTXpgDSjxxhz5o8fv9sPxu/P8yRnSV0SQ2hx4iGa/F9eNMQAFySyE3vr8PmaGIJ7QwhW2o6P7KokZFpJf6TuuAptWPdWdyifupoIyFzeqHtHkT5l+m4KxvwfejkWIpK8cvxoyqkitBU34WyuqIcgPDEpPo7GUJgwtNQvB8OLGt0/IMFFfT84y4AlptmMOLW1+tt9/xP+zDbXdw/vm7qe61Sy7sT3sXsNPPE2idqHWObQzXVSM76L35ej5eVS1dgEvT0HHPcAdr8SybVGwsIvKIbL+mcaMWG74KMKi3/692NfCmEv+9cAYDCAyq3B/Pvf2l8cjk11p2GLs6GUCjLBLfNl5wvsm5Ump/aJ/yqXdV1ts++cQoOjYUf5m/njwXpKFUiV/xtIIOmdOHCGxpfUuoXG8AdYxIByLapuf3t+kvinGlkUdP5kUVNWyF/D84rJEnCur0IAFWYvsX9BVEg8KpkBKVI+RfpvhIE5xhHv9qAIAjEXXN8Scjt9CWWU6ob8SeKGwZxw2Htq40mtYzU2NEILp51Xc/tRZdTUFW/ONydWwmAoh4REWuK5flRz7M+fz0vbX4JV0PWjRP48fCPpDnS0Hq0tSUPTmTrD2vJdhZx8YUTUah9PjNVf2RTtSoH/6kJuPuFYPdKbKisZtauw9y1J5NvC8tZXlJZZ5x+wQncFJDHEmsCdo8Xb5Cv1p1xTU25mrwdsP8nsFUcf5+sZfDVHF+tp+Cu9Z+AxwHOKsjaUO9uq9vnl3SsptUxAowmUqYEojMHAHDhrO5EdvVn6GWJ6E3qk4c5hYk9I2qfr8mHhb9vb7JPRyOLms6PLGraFHn9qT5s+8vI+fsaPNVnp8m5NVT9kUPVHzn4T01EHevXdId6UBhUBM5MwXGkEuuWs9fPoLWIOV4qteUYYo47T7ucPuGhVDdxERz1IORshqPrGmxiWPkYNlUgy7xDAIH7F+2ot90b1/QnxKhhxtvrOFpafcr+kdEjeXTIoyzYv4DhC4fz69Ff8UpeCqoL6lhvJEniw90f8uiaRxkcNxiA6uq646Ut38yy7avo5hdHrwv6AWDZmE/lz5n4jYvFb3QM/iolz3aLxl+pYEWpmW+LKrhr71Fmpx3hq4K6y5HJeiUelJidFgKv+plqkx+iV4L0n+GDi2HRtfBiPLw9GqpLYOPb4KoGpf74MtSJbPkQ1r4GQV0hZki979ehikMAdAs4tajjpePG4hW8uGPLSR4Sccr+xhgYH8jyBy/gygHRADz6S95ZKSDOxjnJNB9Z1LQRUiPhjuczzpwqSj/aA4Ajo/wMz6ZxvFZXkxEtAPZDFZiXZ+J3YSx+o6NP65jargFoewZTtTrHV3DzHKF0fyZ+UgCaZP862901okalaSLyq9tECOsJf75ed7utHI6FOuftQNdjIvPvuZxRSSEcLq7GUk+iw4QQA1/dORylKDLj7fXsLzCf0uaa7tewcOpCRkSN4MFVD3Lhlxcy4esJXPfTdWzK34TH6+GlzS/x6tZXua3PbQxyDyIwMJCkJN8ymtNqZ8k7X7L4z6V08Yvi6rtvAMC6s5iK7w5iGB6JaUJ87fFujQklfXQf8sf25b894ng0IZKLgkw8sD+LV44U1F5YewV3R8DL0zu/x+t1oLb68vGw8BoISvT5w0z4J5Rnwstd4Y8XYcwjoFT5HILBZ70pPQSfzYAfHwClFu7ZBIbjYvNECqoLEBAIrAntPhFRIXJw+lIODPm9gX9c4ySH+/HKVcezR+84cnaJedlS0/mRRU0b4a12IWhal3L9XETySph/y6Lsi+N1X7Q96v8RPRuw7S0l758byJ+3kYofD+Muqz8c1uv0ULYoHU1iAKbx8fW2aSmm8fF4Kh2ULkw/J35QJUkib8E2XF4HCdPqOrW6HccsNU2IGkGAkQ/4oqGK9vm27VwEL3aBd8dCdSlU5kBlLn1i/Lm8fzTlVifD561k3s/7KKis+/+LCdTz1Z3Dcbq9XP32eiqszpOPSO+Q3rw69lWeH/U8Y2LG8ODAB3F5Xdyy/BYmfD2BBfsX8MTQJ7ij1x3s37+fXr161UY1rfh0Kdvz99I/pDvXP3gzKp0aW3oZZV+ko+8XRsClXeuNrBEEgSsjgri/Szgf9Ulgblw4r2QWsLzUJ7z6hyRxf0gJi229WX74BzYOCMAy8z8wewncvsrnDzNyLkx8BjT+MOUVuPAxEJVgzoNtn8BLCfDvAXBwBfS+Eu7b2midrWJbMSaNCX+Nf737Q/2DybK0PpmeIAjcMcpX4HLploNNtO5Y5Oinzo+cp6at8EqIOvntPIa7xIZ5+fEfPr8xMYhnoeiTJAl3oRVXfjVIoEn0p3pLIZb1eRhHRmEaF4eoPf5/te0sxmtxEnhnKoKibX4A1ZEGgq5JofTTfdjTy9F1D2q601mIvdJCxivL8ThchIhR2Pp4UBvr+hu5nU5EhRJR0YzPQmzN8og5F8J6gL3G56QgDRbO9PmGDPVVrb5yYAwjkoL56M9MFmzI4v01R7isbxQPT+pOhL8vB02IUcPobiFsOlKGXl3/d1UpKrm066Vc2vVSAOb0msOGvA0s2L+Ay7tdzkVxF/HGG29gt9sZNMiXWK5gfzZb83YzLL4fk26eDoDjSCVln+1DmxJI4IxuCI04BR9DJQo8nBDBhkoLD6dnMyrAyFeF5bxe4isO+UwW/FOrQJ8yE8STlu8G3ex7HCMg3pePBgABxj4KocnQ6/Im56FT6qh0VLK1cCsDwgaQV53HnGVzqHZWE+0Xzeyes/ki/Quyq7KJ9Yttcrz66B4dCGSxKqOMJ1o1QvsgiiIez7ljMT0fka/CbYWE7FJzAlW/Z4MoEHRNCmUL9qPvH4YjsxLH4UqMI6MQNR3/0ZMkCU+ZHYW/BkHpu8Ou+O4g1RsLam2WmkR/AmckY1nt85mxbi3ENKELhiERCKJA9eYCtMmBKIMbTxnfUrQ9g1HFGLGszkGbEtgp7xiPfrOBQFcodtFKmbKInldfekobl8PetD/NMSpr6jKJSl/22z3f+l77x/qcZAUF+EXVNo/01/HolB7cOy6JLzZn8/Yfh8itsLHo9mG17+efB0vQq5Wolc0zUouCyIjoEYyIHlG77ZgfTUBAAADrf1uLVlAz7trJgC8pY8lHe1DH+hF8XcO5i+pDEATe6B7HkA37GLc5nXCNL79LqlHLXH83XdV/QTxZ0NTHrG/gx4d8gnD2ElA2P9HjsMhhBGoCmbNsDjHGGHIsObX79pft56l1T9U+b62oSQ73+aEdrFLgcntQtbKwaFujUqlwuc4d37/zEVnUtBGSJMkFLU/AU+lAFa7HXeqrP3PM+oFHwnGkkpCbejfr7rUtqfzxMJY/81D4a/Cf0gVdaii2fWVoUwLxOr14KuxoEgMQ1QpM4+MxDI6g8pdMKr47iHV7EYaB4TizqvCfktDmcxMEAdOFcZR+uhfnkUo0iQH1tpNcHtzljlZFXLUXXquLHS8uJswRTYVYQu/nG7YG5OzbTWBkM/2QymssfZ9dCRo/n7gBn9j5v3JAqtcZ1k+r4tbRifSINHH9exv5aksOI7uFIADlVhdxQaf33l1xxRUsWrSIn376iSlTplBQVkSsXwRqnQZPpYOSD/egDNURfGNPBFXLV/jjdBouDPLj97IqjtqdTA315/3eCUALMvFq/WHG+y0+NkDXgK7MnzCfa368hhxLDl39u/LcqOfoGdyTV7e+ykd7PgIgRNeyrM8n0u2EfEF/pB1mfP9TnZLPBGq1Gqfz1KVJmc6DLGraElnV1KLuYqJ6Yz76vmHYdpdiWZuLNiUQw7BISj/ei/1Axy+zVG8tQtvT59dTtjAdFqaj6eqPq9BKxF8GIpxUy0fhryHo6hQMg8Kp+OkI5d9kAKCKqj8T7Omi7RmEMlSHZUN+g6Km6J1duHIsmCZ1wTTWd5cseSUsa3MRdUr0/UJPOY/2xrwqh1B3FDvLVjH077MbbJe1exeHt21m0t0PNm9gS4Hv74AbYfxToNRB+k++PCv1FMc8mZFJIVwxIJqHF9ctDvn3yT2ad/wGSElJIT4+nk2bNtG7Ww+KXOX065mK5PFS+vk+BBFCbux1WtbI93p3YW25hRvTjnBF+KkOu+2NQvB9hgwqA99N/652+18G/QV/jT9vbHuDBFPrxb1aKXLXmETm/3GYW784wNIgf3rFh53utE8btVotW2o6ObKjcFvR+f072wzJ5UUZqsdb7Yu+CL0jFePIKFQxfrhya6I33B0fLaaJ98NTZif4hh4EzkwBwJVfjafCgWV9wyULNIkBhN/bn6hnRhDx8GC0Se1zkREEAcPQSGy7S/FU1b1bdJfacGRWItl96/3mZZl4ayJ93KU2Kn86QvniDIrf340zz9KheW8chypQp/hz0L6DL+c9xo7lP+E9yS/B43az8oP5RKX0pOfoC5s38OBb4aZlcMmrPsuDUg29pkOXkc2e2xNTewIQHaDjkUndeWJqD4YknJ6YFgSB6667Dr1ezweff4yAQJ+RAzCvOIozx0LQ9T1Q+DVzia0BDAoFF4f4kzu2L1NDA05rrNaQHJjMsyOfZfFli0/Zd2ufW1l7zVoCtKc3r6sGHV+6mjq/dYVF2xqVSiVbajo5sqWmrZAk6ODllLOVyl8ysaz1JSXzWJyUfZGO8+jxMFpNUkCtxaQj8bswluK3d2HbWYyhfxjKQA1lC/YDIBpUTfYX1QrEoPa1ghgGhFG5LJPqzQWYxvkiROwHyin5aA94JXSpIbhLfEt6Vaty8BsTgzJEhyrCgNfmwl1kpejN7SCAOs5E8OyeKJpxbq3Fa3XhyrMQOLwbN099h7WLPmHlB/PZ/vP3XHDDzSQOGIwgCBzYsJay3GxmvfgmQjOsLADoAiG+iZIATRBkULP9yQkYNM33o2kOGo2G4cOHs3LlSoyCDo2gonB1LqaL4tDENV0uo7kozpD1VxREpiVNa3B/Q5FRLSEx1MiD45N57dcDAGzYc5hhvRJPe9zTQV5+6vzIoqatkC01tdhr8tEYhkXiyrXgPGom5JbeKIN1VG8txDQutsP9aQA0XfzR9QqmfMkhlGF6NF38ifjbIDxmZ5s7/rYWUa/CMCgc84qj2HaXoAo3YD9YgaaLCZQizhyfpUsZoqPq92wsf+ZhGBSOoFGg0CgIvbU3jqNm3IVWKldkUbUqm4Cp7XehcBXbQAJ1jB+qYAOT73mIAZMv44/PPuC7l54htlcqY2bdgsftsyppja1LVHg6BBpOz2rSEIMHDWblypVEuAMo+yIdRaAGvzEx7XKsc5W5FyWxK6eclfuLeWbJLn46C0SNzWY7o3OQOT3k5ae2RDbU4Myz4C62ETA9icDpSWiTAxFUIo6DFSiDtPhPiG9RNEhbIdUsdwVelYwyVEfxe2k4sswIKsVZI2iOEXBJIgHTklBFGXGX2lBHGQi4ohuCQK3jacDlSUQ8PBjjqCisO4pwHjWj7RGEoFKgTQrEODIabffAOhay9sBT4csHozghTX54YhJXPfkc0x/+P6rLy/js0QfYt8aXrO3orrMvNX5r0eq03D3xRka4k3HlWgi4rGttVJ1M8xAEgeev8NWe2mtWsWV/6/PftAXy8lPnR7bUtBGSV+qUYbhtiafKWbv0YRjoc/pTBuvQ9w/DtrcU06QuSC4vroJq1DF+HWKtkVweShelY99biqZbIH5jYgi9uTclH+6m9OM9RPx18FmXX0hQihiHRQInVT1WirgLfXV5FP4an0ic2AXTuDi8djcKY12LhDJAgzOzfUWNM6sKRZAWUV93iUsQBLoOHEKXvgNI+205K99/C4Co5NNz0j3bCBuRQJVXjafKiS6lc+YXOtOEm7SM7hbCmowSXvhhO193b5uklq1BXn7q/Mi3FW2FnKcGV2FN/o5pSXUicHS9gnEX26j47iBFb26n+K2dOA5WdMicbPvLsO8pxe/CWLzVLkreTcO6rZCAy7rirXbjzK3qkHm0BZp4n6+G6KdGGaSt3S4oxVMEDYDH4kI0tp8/DfiWGjWJDftXKJRK+k2cwr0ffsE1T79EcEzr8pqczfiNiiZgctuH+Z9PDO/q87HbUqpiR0b2GZuHHP3U+Tm7blE7O+e5pebYspKor/ux0iQHYhga4UtyV4OryIo2uQNCVWtqOfmNicE0Pp7KZUeo+OEwglqBqFeiijS2/xzaCOOIKAS1Ak0XU7OsXN4q52lH4TSGJEm4y+wYhkQ22VajNxDdvWe7zMGZmYnzSCaCQkSTnIwqsun5yJxd3DWmK++vOUJptZOnv93Gtw+fGfErLz91fmRR01acAzV7ThdFoC9raeWPh9H1Dqm98AqCQMD0JIwjovBUOqlcdgRndsdYSFRRPtFSvakQv9HR+E9OQB1txJVfjX5QRLtGBrU1x5elmofH4kId3X6izWt1g1tC4d9+wqkhJEmiatkyiv/7X5wHD9XZp+vfn9AHHsAwtP4q1DJnH4Ig8N09Ixn90u9sL1Oy+3AuvRNPr1hsa5CXnzo/8vJTW3GeLz/Z9pVStSqHwKuS8ZidODMr6+wXBAFVuAFtciC6PqHY95Z2SFVqVZge44goKn86jG1vKYIgoO8bhv+kBFQhZ5eD8OnidXh8+WyyzNgPVeAuttYKzfbgmL+OKrx9khE2hNfhIO9vD5P74EOoo2OIeXs+Sav/IOn334h+9V/g9ZJ1440U/etVJG/H50OSaR2xQXoen+LLHzV34bYzMge1Wo3X65XrP3ViZEtNWyFJ562osWeUU/rxXgCqN9QksWtkeUTXKxjzikzynt9EyJxetb4i7YX/JYl4zA5KF+wn6Opk9Kmh7Xq8jkbySpiXZ2JZl4fkPH4RF40q9H3bL0urecVR1PGmNinZYLa72JdnxuryEBOgIynMWK/jveRykTv3fqo3bCDqlVfwv2Rqnf2qyEj8Jk2i7MMPKXrlX3itVsKfePy8d+LvLNw0MpHnfkrncJVI+tECUuIjOvT4KpXPcut0OtHpzq2bnvMFWdS0ERLnb9n6qjW5KIK0hN7Wh8qfjiBoFKgbESqqMD1h9/anbOF+yr86QMBlXdvVv0YQBYJmdqforR2ULdyPMkSHOqrz+NI0hX1/WW0iPk23QESdEkEpoAjUIqrbL1mgx+LE2Ceq6YaNcKSkmld+SWfZngI8J2RBjg7QcefYrlw3JA7FCQK56LXXsfz5J7Hz52McPareMQVRJPiWWxANRgr+8Q80yckEzrz6tOYp0zEoT0j38NIP23n/3skdenx1TbFVWdR0XmRR01acxy41nlIbuh5BKAO1BF/fvJBddZSRoKtTKPsinZIPdhN8U692CYm1bMzHvrcUQSniyvdFZ1V8f4iwO/u2+bHOGDUXfVWMH9qkgA45pMfswGtzI2haL5qW7Mjl4a93EWLU8MTUHoxKCsGoVXKwyMLirTk8+d1ulu7KY/71Awk0qLFu3UrZBx8Q9vDDDQqaEwm8Zib2ffsonDcPw8iRqGM63kdDpuX4aZVU2d2szPFittox6bVNd2ojjokaOQKq8yL71LQV52mZBNv+MtyldjStuJiqY/0I/8tAlCE6zMsycZW0fSZP644i7Onl2PaU1m5zZpo7tDZSe+PzUwqhbOF+bLtLOuSY9owK8EjoerROiH61JZv7F+1gamokKx66gJtGJtAt3I9Ifx2ju4Xy+jX9WXT7MA4UWrj23Q1UVDso+OezaPumEjTnxmYfJ/zhv6Hw96fopZdaNU+ZjufPv4+rff7xyp0deuwTl59kOieyqGkrzp1rZLOxH6qgbFG6r5ZTKytuC4JA4FXJeG1uit7YRvniDCSPF8kjYdtbStlXB7BsykfySHitrtrMwM3FOLRutJDop8Z/auIZKdPQXgiiQNA13dH1DKLs6wN4LB3wg1wTKk8rskNvPVrOo9+kce2QWP51VV/06voNxsMSg1l0+zDyK+3Mf/YDHPv3E/7ww82vHQWIBgOhc+dStXw59vQDLZ6rTMdjPyGA4NsdDReabQ9OXH6S6ZzIoqaN8Nrc552jcNXKLASlQNBVyaflT6SJNxF2Tz/0/cOo3lxA/gubyHt6HaWf7MW6tZCKbw+S+/ha8p7ZQMWSQ00PeAKWdXm1z1URBsIfGIDf6HNvGUJQCARc3g3J7sG+vwxJkrBnlOOucLT5sdylNsq/P4gmObBOeYTmYHd5eOjLHaTG+PPMtN5Nfm6Sw/14bWZfElf/iCWlD/qBA1s8X//LLkUZEUH5Z5+1uK9Mx2J3eRjy3EoAeoSoOFytZF9WYYcdX15+6vzIoqaNcByphHNoSaNZCKCONqLwP/2wYYWfmsAruhF2bz90vUIwXRRHyG19iHp6OCE390ZQ+z6qjiOVOHOan+PGa3ejHxBG9LxRhD8woFPlpWkporbG4uGF6k0FlLy/m8JXt9Rmem4rzCuzEPUqgm/o0WKL13trDpNXYePlq/qiaqaVZ5TeQWrpYT4M7o/Z3vKLjaBSEXDVDCqXLsUrFys8qymo9NUSmzsuic9uG4ESL+/9uqvDji8vP3V+ZEfhtkIA7XlQ+0WSJIr/l4bH7MBTaifg8qQ2HV8d44c6pm4lZ223QCKfGIYzu4ryL9MpenunzyHZIyG5vdgPlOPINKMM1uI/JRF15PG8KQqTBsnpOS8i07w23wVfNCixp5ejCNbitbiw7S5t01wy9oxy9APCWxxZVWV38e6aI1w/NJ6uoc2PPjP/8gtotawO68nHf2Zy30XdWjplTFOmUPLv/2BZswbTxIkt7i/TMXyzLQeAmUPiCPbX0T9E4NdDFlwuNypV+1+u5OWnzk+LLDXz588nNTUVk8mEyWRi+PDh/Pzzz7X7CwsLmTNnDlFRUej1eiZNmkRGRkajY3700UcIgnDKw26312n31ltvkZCQgFarZeDAgaxZs6YlU29/pOMVlM9pvOA8Uomn1I4iQIO+f/vlQTkRUa1A2zWAiL8NRh3jR+nHeyn9bB9li9JxZlWhijDgqXRS9MY2Cl7dSt4/N1C6YB/OLDPKDk4Od6aQXD5/I4/FhSu/GmWgL2pEULadoHOX2PBWueoIx+by7fZcLA43d4xJbFG/6jVrMY4YwbShiXy64SguT8sT6mkSElAnJlK99s8W95XpON787SDgC+kHuHlsdyo9ar5avaNDjq9QKFAoFPLyUyemRdI3JiaGF154gaQk3935xx9/zLRp09i+fTs9e/Zk+vTpqFQqlixZgslk4tVXX2X8+PHs3bsXg6HhH0GTyUR6enqdbVrt8TC+L774ggceeIC33nqLkSNH8s477zB58mT27t1LXFxcS06h/TgPMgp7nR6qVmUjaBQYR0VjujAWQdmxQk5QioTM6YV9f5nPoqMSUdYsf0kuL9a0YhyHKvFaXdjTy1EG6zCOOL1cKp0FhZ8a0aii4tuDCCoRhUmN5PCgSQxos2PYM8pBIaDrFdzivl9szuai7mFE+jc//4fX4cC2fTthf/srNwyLZ8Wf2Xz5zi40VW5cDg/GQC3xvYPoPiwSdRPV1g3DhlH9pyxqzjYKzXae+WEvT1ziSwfxr6uOp1uYNDCJ4O/3smB9JtddNKhD5iPXf+rctEjUXHrppXVeP/fcc8yfP58NGzagUqnYsGEDu3fvplevXoDPuhIWFsbChQu59dZbGxxXEAQiIhrOHPnqq69yyy231I7x+uuv88svvzB//nzmzZvXklNoPyTpnC9o6SmzU/VbNqJBib5faIcLmmOIWiX6fqdaiASViGFAOIYB4YAv0y7C+ZMUUVCKhN3TD1d+NbY9pVi3FhJwaSLqWL+mOzcTZ3YVyiBtnSrszeFoaTV78szcPbZly5UlmzeTr1NTLXnJWbiW2VUqitMr6N0/DLVOSUWhlT+/PsiWnzIZc10KXRuxHOr6plK+YAEesxmFqX2zWMs0n1Ev/obLI9E11HfjOzD+eCJOQRC4rHcYn2wr4eDRXJLi29/JX67/1Llp9VXJ4/GwaNEiqqurGT58OA6HL8riRAuLQqFArVazdu3aRseyWCzEx8cTExPDJZdcwvbt22v3OZ1Otm7dysST1sEnTpzIunXrGh3X4XBgNpvrPNqN88BSg8J3goFXJqMKPf3U+O2NIArnjaA5hjJQiyJAg3VrIaZJXTCOrHsR8Fic2A+U46lunXldcngQ9S13tv7jQDFKUWBsSvNKVBQcPMCXTz/Kp/9+gW0JEaz66WsObnwLR/W7bPWuY8x1SVwwM5nL5vZj1rPDiUwKYNk7u9m+PKvBMTXdfZYARxNL4jIdi0nr+zy9+dtB/LRK4oPr/rbcPiEVLwKfrt7fIfNRq9Xy8lMnpsWiJi0tDaPRiEaj4c477+Tbb7+lZ8+edO/enfj4eB599FHKy8txOp288MILFBQUkJ/fcK6B7t2789FHH/H999+zcOFCtFotI0eOrPXFKSkpwePxEB4eXqdfeHg4BQUFjc513rx5+Pv71z5iY9unnL1UU6H7XL+AWtblIahFNIn+Z3oqMo0gahSgELDtKMa2u6RWwHitLgrf2E7JB7speHEzHnPLw70lwFvtQvK0LNJvw+FS+sUGYNA0bRzeunQJC574K/ZqCyMSexOimUZA7ENc8uBzhA8YRmrRej5+/G9UV5QDYAzUMun23gyYFM+6bw6yf339vzfqON/335mV3aK5y7Qv1w457kIwvkf4Kb+jkYEGevh7WHGwHW9KT0BefurctFjUpKSksGPHDjZs2MBdd93FjTfeyN69e1GpVCxevJgDBw4QFBSEXq9n1apVTJ48GYWiYVP1sGHDuOGGG+jbty+jR4/myy+/JDk5mX//+9912p38QZckqUkR8eijj1JZWVn7yM5upx+zY7/v56imkSQJj9lB9fp8jKOij4cOy5yVKIN1hN3VF0Qo/Wwf+c9vpHTBPsq+SMdrdaHvF4rk9ODMsbR4bNPYWNwlNmx7Wpa5eGd2JQPim67vtfn7xaz65F0GXjKdG+a9TmV1ClZdPJfNHUzKsL5Mv2cuX0VdgaWshC+ffhSr2VcNXhAEhk1LpMeISP5YkE55walh7KJOhyIoCHdh4zdDMh1LRtHxFA0zB9d/4zmyaxD5DiXVNnu9+9sSefmpc9NiUaNWq0lKSmLQoEHMmzePvn378sYbbwAwcOBAduzYQUVFBfn5+SxbtozS0lISEhKaPyFRZPDgwbWWmpCQEBQKxSlWmaKiolOsNyej0WhqI7WOPdqFGktNZ/GpcZfacGRWNqutq8RGwUubyX9+EwCGgY2/5zJnB+oYP8Lu7U/4gwPwv7gL7jI79vRy8EhYdxSjTQlE2y2gxeMes0q2RNhW2V3kVtjoGdn49+/I9i2s/vxDhl0xkzE33ExJTjWHHHH0EPcSGufzCzJqlOijumC56HZslip++vcrSF5fNJQgCIy+JhlDgIY/FqbXzvVEFEGBuMvKmj13mfYnNSag9vmrKw7grSffV2p8KBIiOw/lnbKvrZGXnzo3p+3pKUlSrT/NMfz9/QkNDSUjI4MtW7Ywbdq0Fo23Y8cOIiN96e3VajUDBw5kxYoVddqtWLGCESNGnO7024az2FLjtbrwOn1px515Fsq+TKfg5S0Uv70LR1bT5tzyL9NBEAiYnkTgjGSUwXLl2s6CIAqowg34XRBDyJxeCGoFqlg/wu7tR8hNvVvs7Atg21WC6KduUa2vo6VWALqENBwB6bBW88vbb5DQbyAjrroegPXfHsLPW05XY93lpC4hBjJdeqbe9zeO7trOzhXH00qo1ApGXd2N3PQKsvaeKl4EUYHX3PzkjTLtzx0XJDIkwZfja9ORMt5fe4QDhVVsOnL8/zeom883bMeR9s8uLC8/dW5atI7w2GOPMXnyZGJjY6mqqmLRokWsWrWKZcuWAfDVV18RGhpKXFwcaWlp3H///UyfPr2Ok+/s2bOJjo6ujVp6+umnGTZsGN26dcNsNvPmm2+yY8cO/vvf/9b2eeihh5g1axaDBg1i+PDh/O9//yMrK4s777yzLd6D0+cs9anxVLso/NcWBJWI/yVdqfz5CJ6y4+bb4rd2Ej1vVKPz9jo8qCIMGIdFNthG5uzHVWBFcnoImtHttBLxiUYVkt2N5PYiNDP5Xn5NltiogIarLW/45gucNhsTbr8PQRQpOmomZ385/W1bUITV/ZmKCtCxI6uC+NTB9L5wImu/+ITuo8agNfgS+sX3DiY8wcS2ZUeJPyn03HHgAI4DB4h64SyJmpRBqRCZf/0Avt+Zx7urD/PcT/t47qd9ABjUCnY+NZHIID80gpvDxW2bHbs+1Gp1+waVyLQrLbLUFBYWMmvWLFJSUrjooovYuHEjy5YtY8KECQDk5+cza9Ysunfvzty5c5k1axYLFy6sM0ZWVlYdx+GKigpuv/12evTowcSJE8nNzWX16tUMGTKkts3MmTN5/fXXeeaZZ+jXrx+rV6/mp59+Ij4+/nTOvc2QzlJLjafSgdfqxlPppOzzfbXJ2QBME3zvnS2tBPuB8nr7Sy4vkt19fiQVPMdRhupABMufeUitSF53DE2iP5LLi6sen5WGKK/23fUG6uuvE2U1V7Jj+VIGXjIdv+AQAHavzsUYpCHSmwNuT532wQY15VbfmKOumYXH6WLbT9/X7hcEgb4XxZKXUUFZ3qnzVCe2LPmfTMvxeCWySq28t+YwazKKm2wfbNRw08gEHpyQXGd7tdPDwWKf75de9FJpa38Lirz81LlpkaXm/fffb3T/3LlzmTt3bqNtVq1aVef1a6+9xmuvvdbkse+++27uvvvuJtudEc5Sn5oT6xyF3NIbTdcAvFYXngoHikAtVauyKVvgC5MMvTMVTZfjUU32A+WUf3cQj8WFaVz7RI3JdBwKPzWaxACqNxXgzKoibG7/VlUqd2RUgEJoUQ0ti8ONViU2WOsp7bfl4JUYMPkyADwuL4e2FZN6YQyKAjXek5a3tSoFFrsbAENAIL0unMDOFT8x9PKrUSh9P2mJ/UJR65RkbClk6GXHRYyme3f0A/q35JRlWsG4f62qXXZMDDXw21/GNqvf5f2j+dvXdWs9peVU0j3ChFoBdlfrBXlzkZefOjfyLXhbcJZaaiybfM7VolGFOt6EIAoojGrUMX4oDCrC7utP8I09UcebKH4vDev2otq+tn2leMrshN3Vt03rBsmcGez7ynAcrADAVWxFcnka71APkseLdUcRmq4BLfKtcnm8DQoaSZLYs2oFycNGovPzORLnZpTjtLnpOiAURUAAttJCthdtJ70sHZfHxVdbsqlyuGvH6DthMtbKCo7s2Fq7TaEUSUgN4cjOulYCT0UFioCAFpy1TGs4JmgAEoKb//thq+dz+csenx+N2+tbqmpv5Oinzo0cm9sWeM9OS82x+jymC2PrLT6oCtOjCtOjTQqk/JsMyr5Ix5ZeRuBlXWv9bMQmUs/LdA4Ere//73dhLPq+oYjNyBdzMs7sKtzFNvyntmz5RiEKeBqoYF+SlUl5fh4X3nh77bac/eXoTWpWW3+ltHI9XQ5X8NDPswHQK/UERw1HqBhW2z40rgvBMXFkbFhL0qChtdtjewaRvrEAq9mJ3qRG8nhwl5aiCAlp0fxlmsfu3EpKLA706rqfrX9f13zLWFpO3ajM20Yn8NXWHCRJwuIRCWmBhbC1yMtPnRv5itUGnKWrT7UTUoY3nv1XUIkEXp2MplsAFT8cpuidXagifIJINLb/j4hM+6PtGoB+UDhVa3JbHZZv+dMXTqsM0LTs2CoFDre33txSmTu3odRoiO2VCvZK8HrIOVhGnt9BXl3/L+6P7kb0JgvfXrIYi8fKurx1fJj2Ocau6/gzN5aR0SMB6DpwCLtX/VrnGFE1IesFhytJ7BeKK78AXC7UsWdJvbhziINFVVzy77qZ4/vG+FNicaJrQZSdeNKSqMPtpcLqYseRIhySkoSw9i9vcWz5qTm50GTOPuTlp7bgLFQ17lIbZYv2o00JRJPQdAZgQRAwDAgn7I5UPGV2bDVme6/N3URPmc6AI8uMK9cCbi/u8pYnMJO8Evb9Zej6haIMaVlYf6BejccrYbaf+lnK3rWV6BA1yv/0hxfiML/UlfzMEo4odvHO0H9w7aS/IrjcxFaq6BfWj7v73c0E079QexK5Z+U9/JH9BwCxvftiraygLPd4gk1joAadn4ribF8It+PAAQA0XWVH4bam1FJ3uWZ6vyiOlFQzuXdEi4SB1Vn3M/LJ+qNcNzSOAzm+JajB3do/ClOt9jm0y9aazoksatqCs9Cnxp5RgeTyYpoQj9CCdWhVhAH/S30/+sowPQpj/RErMp2Lyp+O4DE7fRa5FuSYOYanwoHk8qLvH9biQqahfj7LTpG5rpiS9i2lYN82It0HoftUpCve4/+6X4rSq+UmTxYjvr4brcPnJ2PfnXb8XCxqukn3MTZ2LH9b/TcOVxwmMskXNVN4+GBtO0EQCIwwUFlorRljN4rAQJSRcnqCtqZPTN0bp+925GG2u8kutzbQo34+XX8UgEE12aen94vi2Wm9Wb0vD43gpn/XjiloCbKo6azIoqYtOCZqWhFN0h7YD1VQ8Z3vx92a1rJ09gCGwRGEPziA8AcHnLFK3DJti6aLP16bG1euhcqlR7DtL8Nrbf6PtqfCJ0iUQQ3nmmmIuCDf8ueJzqPsWIjt8znY3ErCZjwLU17iK43EnmKfT0WP2W/DgNko/ngCTWQA1Zs21XY9WlZNl2A/5o2eR5Qhir+v+TtKrRb/sHCKszLrHNsUqqOyxDf36k0b0Q8aKC8ptAKX3U7h4YPkHdhfW5riRPRqJRsfu4iDz01mwW3H/ZpWHyihrLp5TrdpOZX8nl6MXq3g39f159npvXn5qr6AxIZsG72DQKlsecLIlqJS+ZbcZWfhzonsU9MW1C4/ndlpHMO6uQBBq8Q4Mgp9v+ZVRT4RQRDkiKdzDNOEOFAIVK30VbG2rM1FNKkJvaV3s/7Xx1dYW/4hD/PT4KdRkl5Yxfie4ZD5Jyy5h/IuV0BGAQHxyVQ6Knlj2xtMCZ4JgD7EH6a8DH4R6Le9TtXK5UjPPINHgoNFFqb1jUan1PHPkf/k+p+uZ3HGYgIioqgoqJt9WG9Sk5tejqeyEtuOnYQ/9miL538+U5J9lPVfLeDglo14PceXhqK792LYldfQJfW4E3C4ySd4d2b7RM+i24dx68dbeHVFOs9O79Pocaodbu76fCu9okx8MGcw4SYtNwzz5dL64o9dlLrVPDqsY3yhjllqZFHTOZFFTRtwOj/4bYXH4sS+rwxliA7rjmKUoTr8J5wdyQllzjyCQsR/Qjz+E+KR3F7cFQ5K3kujak0uQTOSm+wvOXyhts3NInwioijQM8rErpwKsJth8a0QNxxL8ixY8TJ+IaF8sO8TnB4n46MvZgu5aI5F3Y16CNPmrZT/bzu21T+T1X00dpeX3tG+5Y4+oX2YkjiFd3a9w1+DLqEsp27RWq1ehaPaRdWvK8Htxu+i8S2e//nKjl+W8vvH72IKCeWC628iOqUHolJJ8dEj7Pz1ZxY/9yR9J05l3JzbERUKvtmWQ4S/lvhgn2UuOkCHViUiNuN38d01h8mtsPHpLUNrxRFAtd3Jv349RJQGrhjZq93O9UTk5afOjSxq2oKzwFJT8d1BbLtLa1/LodgyDSEoRVQhOnR9QrDuKG5WlIe72IagViD6tS4abkhCEJ9tOIq06kUEewVc/ja2TWmICgWCRsUX6V9wRbcr0FkNKJTi8cSAgoDurrdRLRxKxfzn2Xj/56gUAv1iA2rHvr3P7Sw9vJQCqQzPSUsjxdlVOO0eyr/+Gv3wYajCw1o1//ONTUu+Zs2Cj+g/+VIuuP5mlKrj//ewLon0vGAcO1f8zO8fvYOtyszWLlP4aIPPCji9XxTRAToWbsqi3Ori5pGNFzRef6iUN1ZmcO+FSSScVB9s7nsrKXEp+eDanohixyyFy8tPnRvZYaItOAschb2O40mr/MbEEHpn3zM3GZlOgbZ7EN4qJ86spgs8eixOFCZ1q62RI5NCUFqLkDa/CyPvh4BYXHYbKo2WP3L+oMxexsyUmb7K2icdQtCZCLh0POa0UgrX/cTA+EB0J1iMEgMSGRwxmAPl6ZhL6ibbq67wZSO2bd9O4HXXtWru5xsHN29gzYKPGHrFTPpdfANHdpaRvrGAwkxzbQVtQRDoN3EKlzzwCBkb/mTfz1/X9v9uRx5DE4P44M8j3HFBYr2FTCVJ4r01h/nwzyPct3A7QxOCeGB8XYvhY5/+zsocL7P7GBnbt2v7nvQJyMtPnRv5dr4tOMMh3a4iqy99fQ2KIG2rUuDLnF9oEvxRBGmxrM9DE9+++T8GxQdyh+43XJICzVBfIVqP242oVLL86HJ6BPUgMSCR3cpcvPXktAl84FlKF//KmN8+JOzxyaeMPzVhKns/fw/JW3d5LKFvCIUHitF2S8Lvoova9RzPBazmSn55+w2iUgaQfSCZnb9vrLPfGKhh4OQu9BoVhSAKdBsyghFXX4/3i8/I1MVTqA1nxsAYBsQF8s22XK4fVv8S+C97Cnh2qa9oZbcwI29c0x9FzW+Wy+PlhR/TWLDHythQG/937an/7/ZEXn7q3MiWmjbgTGoaV0E1lb9kAhB2X3+Cru+OYXBEx09EptMhiALGoRHYdpfUKXZaH16LqzYrcWtQChIzlX+wRBqNQ+UHgKhQ4PV4WJe7jgvjLgRArVMgSeBy1E2XrzD5U33hQEyHq7jYeuCU8cfGjiU31HbKdvO23Sic1YQ9/AhCBy1fdGbWLvoUl8NNacEQdEYVU+9J5dZXR3P7G2Podk1X1JF6/liQztK3duGsyTs0ZPoMNOGxjCldA5LExb0iSMutJC5IT5R//dFyH6zNJMpfy0tXpvLdPSNr/Wi2ZJZx+Vt/8tHGXC7wL2NcqK3Dlp2OoaypHyZbajon8re8LThDqkbyeCn7Ih37Hp8vjdfmRt8nVLbSyDQbTWIAuCVchY1X3XYcqTw9a07WevxcJSy0j+DbbbkAKNUaXA47Vc4qhkb4woB1fr67ZFtV3btkl8fL0+HT0YW4sDz1JK6iojr7g3XBaP1NeE/IgG1PT6ds/TZ0OgXG0aNaP/fzhMqiQtJ+W46gHMwF1/Zj2gP96dInBI1eRUZpNbcv282jeXks8XeSk1HBV69tw25zIYoKVvoPJdxZTII1k7TcSlalFzGhZ3i9y5UHi6rYlFnGo1N6cPXgWAwaJbkVNu5ZsI0Zb69HQGDxXSP4+7SBHD58iP3793fo+yCKolzUshMji5q24Az41Fg25JP7f+tw5R+/GJ34XEamKSS3F2dNtl2PpWFTu7vSgafC0azM1A1yYBkYI4jqNZJ//3YQm9ODzs8Pr9uNxqukV4gvssUv0HfHXlVa1+qyaHM2Oyt1BE2LA5edrJtvxpVfN3w7QgzGqvJZD+z79pF16604A6II7CFXmW8Ov76/CFAx7qarSb0wpo4gMWiOW+kOCB4+UVkpPlrFo0+uIa/Chi0ogTxNBH3Nu9hwqJT8SjtbMsvqPc5XW3MI0KuY2MtXriOr1MoVb/3J5iNlvHJVX5bcM5J+sQH06NGDbt268eOPP2I2m9v13E9Grv/UeZFFTVtwBiw11RvzwSMhGlX4T03AeEE0+v4tz0kjc/5S8cMhKr4/hCrG2KgVxpnpu6Com2mp8XoduN1V7N33CFbrEd/GI6shcSx/ndST4ioHr688gCEgCIBuqng0Cl/WYb9gLaIoUFF4PFFfodnOK7+kc/WgGEyDJxF3USWS1caRy6+g/Msv8dbcUfs5NVSJVgrfnk/mtdehCgvHEZGMf6Rfi9+b843yAguZO9cSnjiMPmO6nLI/PtjArw9dQGyQr0RGv/5hBI4KI8kCby1M45cHLyDN1ItYex73D/FlA+4VXb8IfuePwySGGNAoFRRV2bn+/Q3o1Up+nDuKGQNjaus/CYLAZZddhiiKfPLJJxSdZJ1rC/5c8QsvP/1/fP3RBxzav49/PvUU/3zyCaqrq7HbW15ORObMIzsKtwUdbKlxFVlx5VcTdF139KmykJFpHe5SO5qu/oTeltpoO+dRM4pgLQq/hktm2O15VFRsxuEs5uDBebXbRUFF965PQMFuGHAjCSEGHpjQjZd/SaffZV0A6OI5HmatUIoERhooronIcrg93LdgO1qVyN8n94DCcjTa5+nyzosUvvMFBf/3FIUvvIgmKQmFZGFImZOy1W8RdP11mO64F/NjmwmNMZ7Gu3R+8OuHK0CyMXbWpQ22SQrz49u7R3Lrx1v4fX8xM67rz679lRj3WvB6vBzWJ+ASlHz/3c9AMlcPqmshK692cs+CbQAMTQwG4MnvdmNzelly73DC/E71v/Hz82P27NksXLiQ+fPnExMTg7+/PxaLBbvdTlJSEhdeeCFHjhyhsLCQ+Ph4YmJimnXOBdlZ/Lp6DZKoYHdmFrszs3w3pgrfZXHdunVMnDixWWPJnD3IoqYNkE4Ic+wIyr/JQBGkRds9qEOOJ3Nuok0JpHJZJl6bu8G8RpIkYdtbijJMj7vC4QvrFgUcR804s6owjoikKP8Xdh+4t06/yIgryS9YjE4XB8X7QPJApC/NwJ0XdGVHVgUPLj3KTUqJoOq6Vb/Du/iRf9iMzenhvoXb2JlTwee3DiXIoIYInwBT2jOJfuVlQu6+G8tvK7EeOoT1cBp5XVx0ee1leqRO4mhN3qawLu1f2bkzU5JjIXffDrTGAKK7pzTaNsSoYeFtw7j90y08uWQP1w+MRPVLPs/N34pbVFEVnEhkVSZoklm0KatOPqF31xxmR3YF784exISe4Szbnc8vewp56/oBRAc0XCQ1JCSEu+66i7S0NDIyMrBYLBiNRgICAvjzzz/Zs2cP5eXlKBQKPB4PKSkpFBYWMnbsGPr1619nrMKcHEBi59atrNu2DUEQuO2mOaz4/juKS8u48rrriYyJ5bWHHyBarhHWKZFFTVvQQZaaqtU5OLOrcGaaCZqZgtiK7K4yMsfQ9w2l8qcj2HaXNBgxV72pAE+Fz6em4IVNIAooAzW4S32m+cqlhymLXwspkJBwP05nMS5XBUlJj5BfsBinqxTMh3yDBScBvgzDb17bn3s+30ZJsQvNoUpKLQ6CjT5xE50SyN4/87nq9TUctth5e9ZABnWpEfD6INAHQ5lvWUuTmIAm8VacGelIT/yFNakuRtSsNmXvL8PgryYgXN9O7+C5Qdrv2SDlkNCvP4Ig4JW8rMxaybcZ37KndA9Wl5UIQwQjo0dyfffriTXFMiY5lDUZJawvM9PNT8S5rxL8oGvffpT9vhhFrJv+cQG1x/jjQDEf/HmE2cO7MKFnOJU2F08u2cOEnuFM7t10tKZSqaR///70719XpPj5+bFmzRp69uzJjBkzWLBgAenp6RiNRr77bgk/ff0VKpUKpSjidLuxCcd/M3WSl1vuvoeQiEjmzH2wzrjdIsLQGuTPTWdEFjVtQTv61EhuL86cKqw7iqne4HOMVPir0SSehtOmjAygMGlQBGlxlZwaCn0My5pcECBsbn88Zieecjuu3GrcpQUA2PwPUpyyCAEViQlza/t5PA5UqiCyst4jQXEzSq0/6AJq92tVCt6ZNZBbDtjpdtTMkOd+pXukCaNGSU5hNTMRSHCIvHrPSJLDT/KJCYiDyqw6m7L27EKl0VJqcmJ1W5EkiSM7iolPDZELWDaC2+khfXMeXlcx0d17UFhdyCNrHmFr4Vb6hfbjmpRrMKqNHDUf5ecjP/Nl+pfckXoPry33WTF2ZFXQu3sowZsrCPEIvLvfywyvhxBnKY8sTmN0t1C+25HLy7+kc0G3UB4Y3w2AF37eh93p4Z/Tep/W/+eCCy4gPDyc5ORkRFHkuuuuw2q1smnxAjbt34kiMhav10ulJIAkkBgaSFlpKX7+/lx7y23o/eq34qm0OlwO2aemMyKLmragHS011u1FlC/OAEAVYyT4hp4oAzRN9JKRaRrJ48VT6URhrN9XxmNz4S6xoY7zQx1phBOs8f6XJmLbUYxeryerCDTOuqZ6hUJDfPwdHDw4j+qidfgbTvX9UogCuSFWUg+ZeGKokf1uf2wuDwPiAwnaWcUYQThV0AAYQqG6tM6mI9u3ENurD5KYzuf7Pme4eBHmEjtJA+SyCI2RtacMl7UYSfIihRi4/qfrAXhv4nsMjRxap+1fBv2Ft3a8xX92vI4UPBQKplPlcEOEDrWuipeHJnHrJheSIHBtkoLXc2DEC78BcOeYrjx8cQqiKLD+UCkLN2Xz7PTeRDSQx6a5qFQqevfuXftaFEWMRiNKpQpVVTk3PTOPoKgYtq75A6OfiZSTlqMaHFejwVJuOa25yZwZ5OinNkCqsdS0R34YRZDvSx90fXfC7+0vCxqZNkGSJCp/zgS3F21yQL1tqlblAGAce2pItKhWYBgSgWAQwSvi5z31YhEfdytKZQDVZduQNKfeEQuCQEmQC9GgJaHqIC/OSOXNa/vzyKTuDJ8YT+Fhc63DcB00JnAcD/G1lJWSm76XpCEjABgQNoC0VTmYQrTEpAQ24904f8naW4rezxdp9n/7X0Sn1LFg6oJTBA2ATqnjL4P+wv8N+wfqwI0ERq2mf1wA89ccxhaspvSQmeToIBwaf3r7uUmN8VmTp/SJ4O+TuyOKAnaXh8e+TWNwl0CuG9J+VbeHXn41giiSs3c3AANHj2m2oAFQabW45OinToksatoA77FEYYq2FzWC0vcvUgac3h2NjMyJWLcWYlmbi/+UBFThp9bmAbBuKURQieh6NOyQXpm/E0QvXQffd8o+SZJQq4NQiUZQ1i/G1SoNmt6x7P1jJe4T8oIkpIZgCtWxeemRUzvt/hoy19S+TPt9OUq1mi6DBgHQXZHKwS2F9L0oTk5E2QS5ByowBrrxqkRKpAreGv8WYfrGrVtXpVxJf7+rcZmW8dClaroE66n0U1CUaeaWYfGUoOdQZg7f3T2S+dcP4IUrj0fXvbkyg9xyG/OuSK0N3W4PNHoDwTFxHN6+pVX91bKo6bTIoqYN8NakC1cGtq3wcBVbsaz1ZV8VNLJTsEzb4Sq0ojCpMY6Orne/JEl4q12o402N+jxUe331e0TlqU6VFZVbsFoP42fshXDM7+wk/DX+uPqEYbNUsfePlbXbRYXIkEsSOLKzhOz9JyVxE49nDXY57Oz4ZSk9R12ITeECCSyrjBgCNfQcJUevNIbT5qai0IpVLKJa6eDRoY8S69e8RIUvjn8IyR7DE2ueJrPUQlKPYCQJeul12BQ6KsrKEUWByX0iMWl9/6+9eWbeWX2Y+8YlkRTW/mH2epM/xUcPt6qvSqPF5XC08YxkOgJZ1LQFNSHdtPGdR+XSI9jSShA0CkRZ1Mi0IdpugXjMTsoW7sdrPTVzqqfSl9BOGXo81FbySqfUiCpw+qozH/jzeWwVdZOjlZdvQKk0oTHEgbv+u95gbTClehspw0ax/ptFde6Ok4eEE9UtgN8+2Ye9+oQ59rgUEi4AYNtP32O3VDF42gyKbcX0KBqO5ZDEBTOTUark70xjlNVkID9YtQeFVsMliZc0u2+kyUCE+1rKXEdRmnYSG29CFAW+WHEIl6gmQFX3c+L2ePn7N7tICjVyx5iOqbgd16cf5uIiSrKPtrivUqPFZW/YgV7m7EUWNW1BOzgKO/MsOA5Xok7wJ+IvA1H4y740Mm2HNjmQwKuSsR8op+BfW3BkVtbZ7zhUAYCoO24VsazJJffJP7HtO+6kO6zPr+gc3SjR/0jGhhfqjFFVtRu9PhFBFwi28nrnEesXS3ZVNqOvuxG72czaRZ/U7hMEgfE39cTl8PDz22m4nDVFLm3loA2gLC+XDd98Qf/JlxEQHkH6rhxGHZlBtxEhJPSVk1I2RWWRz5em1FVIqLFunSbJ66Xqt9/Je+IJjs6aTdbNN1P44kvY9uypbaNwxREk9EUT8gdbssrRBqg5mmWmZ9U+LNl1LSQfrfPVhHrhyj6olR1z2Rk4dTqm0HDWf72wxX3VWq0c/dRJkUVNGyBJEghtl3xPkiRKP96DMkhL8LXdUZhkQSPT9hgGhhPx0ECUYXpK3t9N0Ts7Kf18H9VbCjGv8N3dWncdt75Y04oBKP14LxU/HEJyezGEJuCvHgBAUNDIOuObzbsIChwBxjCoLj6e+uAEEv0TOVRxCFNoOKOunc22n7/nwMY/a/f7BWmZelcqRUfNfPevbVQWW6G6GIc6mB9ffwG/4BCGz7iWPWtyyf9KQVHgES66rvcpx5E5FUuFA4/aiUGrx3DC8qE9PZ0jM2aQc/fd2HbsQBkWhmgwUvnjD2ReOYOcBx/EXV7OgLhAvJUjETUFfLV7LdkOJ+FKJf4RUXWOk1Vq5ZXl6cwZ0YX+cR3nuK1Uqeg2dAR5B/a1uK9Ko8XjduNxu9thZjLtiSxq2gKv1KY5aiSHB0+lE78xMShMDaeml5E5XRQmDSE39cYwLBJBKeLMtVD+9QEklwe/cbF4SuxU/HQYr8ONq6Aa/ykJiEYVlj/zsKzL840h+RyN1abjFyynswynswitPgkC4sHlEyMn0yO4B+WOcvKr8xkwZRopIy7gpzdfJmPjuto2kUkBXP6XAdgsLhY+vYlfDozl81/KqSgoIHnEHL57dTerPk+nrMsRSsfsRNFBloDOjsVsw6KopEtwV9w19bMsa9aSec214PYQv+Bzuv74I9H/eoWYf79Jt99/J3LePKzr1nP0mmsZG+AhOzcGvSKQEmkTxU4XkVo10cndie7eE/DdoD3+XRrBBg1/ndh4tuL2wGmzYgg4VUhJkoTb6ayNXD0ZldbnHylbazofcp6atsArtak89FT5fmBEWdDIdACiWkHA1MTa1+5KB6JWiaAWEdQKzCuOYlntc1jXJPqj6xtK4WtbcZf7fvC1+hiwg+aEqJm9+SsAuHXVU9yXOJspAMX7fVabE+gb6iudsKVwC5d1vYzJ9zzIz5LE968+T59xExl6+dX4h0UQFm/iqr/357f3vmTvpgN4JVAbp7F7tYOY7oEMvzKRmdseZ07EnPZ7o84x8ioKcIp2kiK6k5n+J7a0NHLuuw/90CHEvP46oq5u6QJBqSTg8unoBw4g6+ZbSHzpUQy9biVMMQiL31YEBAweAVuVGa3Rl1/om225rMko4cObBmPQdPzlxm7xUJyVw9t3PYTbacXjsuF12/F67IAHURlC4sDxDLn0AiK7HQ8xP1HUaA1y7bDOhCxq2gBJOv2lJ1exlao/cvAbE4N9XxmIoAySw7hlOh7lCf5bprGxaBL8qVhyEG+1G1WU0RcmLQgIgoBtTwmq7bGQIlKavwb/ON/Sz5GiP9B6ISZ4AI/u+4ALBYFDaZ+Tg5UhkUMI0vrCxAO1gfQI6sHanLVc1vUyFEoVU+f+jegevVj35eek/bYcU2g4ap2OisJ83A4HXY2ljPzLfHSR3dH5qVEoRbYWbsXisjAsatgZec86I4WWIhQKkajwBPaZfyL7gQfRpCQT88YbiNqGf3vUcXEY3vwvxddfx1zXV3yaMArRtIIUlwJHuYNqqZyIxG6UWBz8c+lepveL4sKUM5MEUVDEg5COICjQmyJQafWodQY0BgNqrY689K0c3LiIgxu/JCJpBHr/YPpOvBCX3eeY7rLLEVCdDVnUtAVS65efPNUuyhbux3GwAvDlBgHQ9Qtt8xBxGZnWoIk3EXafL3HZMfGuCtNjWedbglLHBWEUe1Gt21vbx1q1Cysm3h7/Lk+vf5pdBW9j3b+Ev5Wv47Y+tzF3wPGSChfFXcQHuz/A7rajVWoRRJH+F19C7zHjObRtE4WHD+J2Ouh5wTgSq/8kOO9n6NG3znduxdEVhOnC6BPSp4Pelc5PmaOUAGUkAeERSJJElcVMn48/blTQABwqtnD9d9n0TL2Cv238lP1HBvNrX8hXW4hwGKgsLCBl+Gie+WEvAvDkJT075oTqITyxL6V5ocx5cVQDLWaSfzCbxfNepChzL15PFYe3LqndaymzERTVQFeZsxJZ1LQFXlq1/OQqrKb4vd1IDg+GYZHoU0Ox7SvFXWTFMDC8zacpI9NaTrZEht6ZivOoGQQBTbyJ/F3heCQrP+z4PywlP+CHhWrdQBSigmdGPoPVrkC38X9oEPhs32cs2r+IQRGDeHnMy0xJmMJ/dvyH5UeXc1nXy2qPodJq6T7iArqPuOD4gf/7EiSMqSNo7G47Px7+keldpyMKsj9Nc/BKXkpcxURIXfETfZcB4dKpqGPqz1t0Io9+k4ZOrWDemw/iePAQd+z9jY39glEFVROu8SNrRzU5kpHvd+bx2sy+tYVKzwQOqxuPp36/mWNEJsVy7/v/qWlvY8M3v1CSU0FuBijUwR0xTZk2RP4FaAMkSWpV5lL7/jK8VU5Cbu5F4PQkNIn+BExNJOSm3mi7yendZc5eBEFA08UfTbyv/IFSacTtrkJf9jlhohmD6CUqZHRte33vqxCcFv4TfwW39rmVhIAEfs/+nQp7BbGmWEZGjeSTPZ/glbwNHRJKMqB4H3SfWmfz94e+x+wwc1XKVe1yruciRdYiqhVmFE4Vjq+/Qe3xUh3fdOK9tJxKNh0p4++TuxPuryP0/rlImUeYXhiMThKQPD5L83932rkgOZTp/ZoWSe2F2+XhwKZCkgY2f+lLo9cx5obpTLz9GhSqONyuRj6PMmclsqhpC1oZ/WRemQ2AQq7nJNPJ0eu6YLHswyYdN/6mxl5+vEFYDwjrybDCQ9yeejvXdb8OAIPKFzl1W+ptpJens/zo8oYPsv0z0PpD0kW1m6wuK++mvcukLpOIN8W37Umdw+RacrGqzLiroeKHpUSERpB7cH+T/RZvy0GrErmou08o6Pr2RdevH0PWl0K1Eqc1C68hgDyXhuemn14F7tMl/2Al9moXvS9oubBS1SQ7dTs8bT0tmXZGFjVtgUSL3knJI2HdUYTk9C07yXWdZDo70dHXI4padIKbHLErSX0XEag/wRlBEGDgTbB/KZQf5ffs3+li6lIragaGD2Rs7Fhe3vwylY7KUw/gsMC2T6DvdaA6HpXz9s63KbOVcV//U2tPyTRMmb0Ms7YEgGqvjsSx48jPSMduabwydUKIAbvLy5srM2rDoQNmXEnoviI01UbKi/aTLkTw4MRkYoNOLZ3RkeTsL0NnUhMUVX9ts8ZQqX2ipjbho0ynQRY1bYDUAkuNNa2E3KfWUbYoHVWEAf/JXdp3cjIyHYBaHYQ29AoAqpwW4oMHn9qo//WgC4Q/XmJX8S4uiruozp38Y0Mew+a28eSfT+LxnnQx2fQOOKpg+D21m1bnrOajPR9xd7+7iTU1r2aRjI8qZxVl+gIAnClDSZkwGa/Hw8EtGxrtd+OILvx9cnfe/O0gT3y3G49XwnjRRdh0oYhuM86qIuwxPblpZEJHnEajmEvtBEXqW2UtEpUCoijgssuiprPRIlEzf/58UlNTMZlMmEwmhg8fzs8//1y7v7CwkDlz5hAVFYVer2fSpElkZGQ0Oua7777L6NGjCQwMJDAwkPHjx7Np06Y6bf7xj38g1ISQHntERES0ZOrtiySBu+m1V+v2Iso+34c6xkjonamE3d8f8QzkbpCRaQ8S4+bwq1nJfm9s/UnN1AYY+3fY8RldKgsptBbWaRdpjGTeqHn8kfMHT617Cpe3pt5TRRasfgWG3AYBPvGyIX8Df/3jr4yJHcNNvW/qiNM7p3B6nHjUNjTOSqwJ/TEGBRPXO5Xdvzey/FfDnWO68tKVqSzclMXchdvx+JnISeiOx7Ebm6jl/tmXoFKc+ftlpVLE627cSbghBEFAqVHIlppOSIs+eTExMbzwwgts2bKFLVu2MG7cOKZNm8aePXuQJInp06dz+PBhlixZwvbt24mPj2f8+PFUV1c3OOaqVau49tpr+f3331m/fj1xcXFMnDiR3NzcOu169epFfn5+7SMtLa11Z9wOSA5Pk8UsvU4P5lU+H5qgq5LRdPE/o+vNMjJtTYg+kh7dHuePwr3M/X0uJbaSUxsNuhliBvNiSTmrM5bwwe4P6uweEzuGZ0c+y9LDS7lp2U3sKdwOi28FfTCMfRSz08yb297krhV3MSB8AC9f8LIc8dRKwsskAsoPUCL4Ii37TpxK7v695B9Mb7Lv1YNjmX/DQFbsK+TmjzZTENIVjyMNKXkIg7uemZw0J/LfO39j/4YCPCfdbO74NYtFz25i3TcHMZc2XrBSpRZln5pOSIt+DS699FKmTJlCcnIyycnJPPfccxiNRjZs2EBGRgYbNmxg/vz5DB48mJSUFN566y0sFgsLFzZcUOzzzz/n7rvvpl+/fnTv3p13330Xr9fLypUr67RTKpVERETUPkJDz56CdaJO2eTyU9UfObgLrQRdk4IyWNdoWxmZzsrsXrN548I32FW8i8uXXM6vR3+tu5QkKuDK9wkUlHxlhs+3/Zdye91il5d2vZQPJ32I2VHJNctmc5mUzX3J/Zmzai7jvhzHR3s+4rbU2/jPuP+gVcr+aK1BrVATl+8msHw/pWUS1ZUOkgYPIygqhrWLPm2wfMCJXNwrgo9vGsLOrEqq7WYEyc1NM6d1wOybz/DLu+K0uTm8vZiCw5X8+fVBSnMsbF+exaePryc3vf5Cq0CNpUaOfupstPoWx+PxsGjRIqqrqxk+fDgOhy/zovaExE0KhQK1Ws3atWubPa7VasXlchEUFFRne0ZGBlFRUSQkJHDNNddw+PDhBkY4jsPhwGw213m0Hw3/CEgeL/a9pQgaBfp+Z/4uRkamPRkXN47vpn1Hr+BePLjqQSZ8PYGvDnx1/EIZGA/Xf0WozcwH2UcpT196yhj9JDXfllp5o7iCwdGj8Gj8CNWFcl//+1g+Yzl397sbpSgv3bYWP7Uf0aUSYd5sBEHg8PZiRFHBBTfcRFbaDtLXrW7WOMO7BvPakCCw7yK6UiKouj1/Y5tPt8HhRHULIKZ7EBu/P8zP76Sx+KWtKDUK7vrvWK5/2pd5+rvXtpO9r6xOX7fNTuXeQ1BSiMvmPBPTlzkNWvyrkJaWxvDhw7Hb7RiNRr799lt69uyJy+UiPj6eRx99lHfeeQeDwcCrr75KQUEB+fn5zR7/73//O9HR0YwfP75229ChQ/nkk09ITk6msLCQZ599lhEjRrBnzx6CgxtOjjRv3jyefvrplp5iy2nCSlO59Aiu/GpC7+rb/nORkTkLCNQG8tb4t0grSePL9C95Zv0zeL1eZnafCcB6wcl78Un8Lecg3b+9Bza8C7FDQVRC/k44uhZFUFfGzfyGcbH1OB3LnBaBmkDCKkAV7keXPsHsWZtH7zHRdB04lJTho/n1vbcIS0giKKrxcGiH1cqBb98GpT89s7bhzM5BP/jM/78USoG8jAq+eXkr+Yd80XRKtcjQSxMQFSIB4XpmPz+C397aQOnMKVQ5zVRH9UaJC22eL7R9IFBUOAVu+NcZPBOZltJiS01KSgo7duxgw4YN3HXXXdx4443s3bsXlUrF4sWLOXDgAEFBQej1elatWsXkyZNRKBTNGvull15i4cKFfPPNN3UsPpMnT+bKK6+kT58+jB8/nqVLfXd2H3/8caPjPfroo1RWVtY+srOzW3q6p41lYz6WdXno+oTUJiqTkTkfEAWRvqF9eW7Uc1zZ7Upe3foq+ZZ8ss3Z3PfbfbgC4zDc8SfM/ByCEuHIH5CxHLQmmPYW3L0eZEHTLkQbo/GvBru/jt5joinNsZCzz7cUM+H2ezEEBvH1c09Qlpfb4Bj2agtfP/cU9uoKKlOT8WhF3KX1+FGdARL7hRIYaUDvr2bMtcnMeXEkd7w5ln7jjxet9AvSMumWnmiclQhIGPPS0Obtp7LPRFyX3ISk1hAXLFfp7my02FKjVqtJSkoCYNCgQWzevJk33niDd955h4EDB7Jjxw4qKytxOp2EhoYydOhQBg0a1OS4r7zyCs8//zy//vorqampjbY1GAz06dOnycgqjUaDRtNBie3qWX1yl9qo+PYgqmgjgTO6dcw8ZGTOQv4y6C+syV3Do2sfZWD4QABeSn0asdwDPS7xPWQ6jHBDOCa7QKXWS98eQYR1MbFhySGiuwei0Ru48rFnWPzckyx4/CFGXzeHXmPHo1SpAF8G9SPbt/DbR+9gKavEEDKDlfELuMxPj7eynhxDZ4CEvqEk9K3f79KVn48rLw9nZib2/T6n6Nj336fSoSWgZzyGCJ/1P2duLl6rtcPmLNM2nPaitCRJtf40x/D39wd8fjBbtmzhn//8Z6NjvPzyyzz77LP88ssvzRJADoeDffv2MXr06Cbbnklc+b6or8Aru8mh2zLnNX5qP1664CXuXHEnWwu3ElOo4/OHfAnzHly4BFFsnjVXpm0QBRGjpKbUa0YQBEbNSOKbV7aRtiqHvuNiMYWEcu2zr7Dq43f59b3/snbhx0QkJSMqlRRnHqGqtJjQLt1R6KYwYHof/pv7PIYiN1W/riTsr38906fXIKUffkTxa68hOU/wlVGpMIwYjvEkNwJRp8NdWtrBM5Q5XVp0pX3ssceYPHkysbGxVFVVsWjRIlatWsWyZcsA+OqrrwgNDSUuLo60tDTuv/9+pk+fzsSJE2vHmD17NtHR0cybNw/wLTk9+eSTLFiwgC5dulBQ4EsIZTQaMRqNAPz1r3/l0ksvJS4ujqKiIp599lnMZjM33nhjm7wJp009LjUei5PSz/YBoAyRo51kZAaGD+TxYY/z+ecvMXRvIDp/f2yVleTu30tsT7m6dkejVWgpsvuWiyKTAuhzYQzrvjlIWLyJyK7+aA1GJt39IIMvu5L969ZQknUEr8dD8rARRKUMZO3iamJ7GLAkZUPNKpUzM/PMnVATVP64lKIXXyRw9iz8L5uGKz+P3PvmYhg8uN70GoJeJ1tqOiEtEjWFhYXMmjWL/Px8/P39SU1NZdmyZUyYMAGA/Px8HnroIQoLC4mMjGT27Nk8+eSTdcbIyspCFI+78rz11ls4nU5mzJhRp91TTz3FP/7xDwBycnK49tprKSkpITQ0lGHDhrFhwwbi48/OWi8ei5P8ZzcCEHJzb0S1fBcqIwOg2lPCsL1BGIZ3Z/bNTzL/tuvJO7BfFjVnAJ3WiN2Wy1HzUeJN8Yy8IomS7CqW/ncnU+5KJapbAADBMXGMvPr62n6VxTZ+/M9OlEoFE27pxev7XiHaGI06QcQ4ZswZOpvGcWRkkP/kk5guu5TwRx9FEAR0vXuhW/0HYgMuCqJOjySLmk5Hi0TN+++/3+j+uXPnMnfu3EbbrFq1qs7rzGYo+0WLFjXZ5oxSI/Ilt5fKn45gWZcHgOnieLTJcrVtGRmAnL27SV/4Pd0vHMeUOx5kx/KliAoFPUadnRfCcx1jYBh+9gJWHF3BrX1uRaESmXpPX36ev4slr22n7/hY+l4Ui8Hfd9F32t3s+zOfTT8cRuenZtqD/dCZVPyW9RsTu0zEa1mKaDTikSQUZ1FiUa/dTs59c1HHxhL59NN1rDKqsIZTbIh6PV5b4wn6ZM4+ZEePNsJrcVH8v104s6oACLu3H+oYvzM8KxmZs4ctS78jJL4Lk2+/H0EQOLB+LTE9+2AKkXM3nQnUgcHE5wTw70M/cEvvWxAEAY1OyaVz+7Htl6NsW57F9uVZmEJ1KBQClUU2JKDH8AiGX5GE1qBiXd46imxFRAZP4PErAtnVYwAVq3bir1QwLMDAnKgQxgb5ndHs6eWfL8CZnU3iD98j6prvCiDqdLKo6YTIoqYN0MSbQCHgzKrCMDSCwMvlSCcZmZPJ3rOLIdNm1DoFO2xWDP4BZ3ZS5zGqqEgi96dxuPIwG/I3MDxqOAAKpcjgqQn0GRvD0bQSSnKrkTwS/mN1dEkNwS/oeLqNT/Z9gRTxVx7OEkkMiWCW6CIhJYFCp4ufSyq5dtdhpob681r3OEzKjl+G91RVUfL22wTOvBpNYmKL+oo1PjWSJMklbToRsqhpAzSJAUQ/PQJrWgm6Xg0nA5SROV+RJAmn3YZGb6jd1m/iVFa8+x/K83MJjGw8yZtM26OKi0MsKKFPQE/m75zPsMhhdS7eWoOKlGGRpDTQf3Phbn50jULUduUlpYtBzz1C16U/oony/QY+EB/Oj8WV/CU9i+nbMljcP4lAVcdeciSHA291NZpuLb/RFHQ68HiQXC4EtbodZifTHsiV4NoIQSli6B8mOwXLyNTD0Z3bQJIwnFD+pOfoC9Gb/Nm6dMkZnNn5i7ZHD3C5uT/wcrYXbeenIz81u6/L42ZOWjqSOo5v+iUxNWMPolaL+oTgDUEQuDQsgO8HdKPA6eKmtCO4va2rmt1alCEhGMeOpfyrr1rcV9TrAWRn4U6GLGpkZGTajbwD+1nyyrMsnvcUAF36Dqjdp1Sr6X/xJexZ9StW89mRtO18Qtu9OyiVJOdKTOoyiec2PkeupeEMwidy25YfKVV05f/ilAwKMGHbsQNtjx4IylMtMd0NOj7sncBmczVvHC1s69NoGq8X56GmawWejKjziRo5rLtzIYsaGRmZdiHvwH4W/t/fKMvNYcLt93H/p9+gUtcNn+07cQoIApu++/IMzfL8RdTr0fXpQ/X6DTwx7AlMahN3rriTElvjpQ4+Sf+RXyzhDNLkc0fSQCSPB+vGjeiHDW2wz9AAI/fGhfNmViGZNkeD7dqa8i++xLJqFf6XXdbivqLe51QsOwt3LmRRIyMj0y5sX/YDAeER3Piv/5J60cUo6/FL0PmZGHH19WxduoR9f/5xBmZ5fmMcMwbL2rX4SRremfAOVpeV65Zex+aCzae0dXgcvL71dZ5K34NKVPDpIF/RYevWrXgqKjBecEGjx7o/PpwApYJXMwva5Vzqo2r5cgwjRxL5z2da3PfY8pPXKouazoQsamRkZNqc6opyDmz4k94XTcbjlbDaHFQ3cHEYdMnlJA0exsr332LH8ub7dcicPqZJFyNZrVStXEm8KZ7Pp35OhCGCm3+5mTnL5vC/Xf9jwb4FzNs4j4u/vpgP932F228898XHEqj21YIy//AjyshIdH37NnosvULknrgwFheWU+BwdcTp4ThyGG3PHq3qeyz822uTl586E3L0k4yMTJtzcPMGspWh3LLGjXPtD3gEES8ig/VmRncL5a6rx6Gs8b8QBIEJt9/Hsv++ysoP5hPbszfBMXFNHEGmLVB36YJ+8GDKFy7Ef+pUIgwRfHjxh6zIWsF3Gd/xyd5PqHZVE2mIZGKXiaiDr+T1HBs3RocA4KmspHLpUoJvuRlBbPoe+ZrIYOYdLmBRfikPdIlo79NDYfLHY7G0qq8g+9R0SmRRIyMj0+Zk7d3Nj+GTCVfYGBguEmhQYHc4WZGrYtMuF9/u/pIP7h5HlxjfhU1v8mfa357gwwfv5JsX/sG4m+6k68AhZ/gszg+C5txIzj33Ur1pE4YhQ1CICiZ1mcSkLpNOaTttWwZjAk2EaXxWmrKPPwGvl8Crr27WsUxKBZND/VlSVNEhokYZFIinrLxVfY/51EiyT02nQl5+kpGRaXNeOuyHU6HhofHJvP7gDJ66/TLm3XclW164mtcuCqHIreaKN1eRX3z8gqNQqpjx+LMERkbz3UvPsHOFvBTVERjHjUPbuzdFL7yI5PE02K7M5WZTZTVTw/wBcOXlUfrhhwRedx3K0NBmH29KiD/7qu1kNcNhWJJaHwJu/mU51evW4y5p3PG5IWqXn2Sfmk6FbKmRkZFpUw4WVZGti0WBl4tHnFqo8vIJQ0mKi+Dq97dw4cu/09tg586LUhg/aiABEZFc+dgz/PL2G6xd+Am9xoyv18FYpm3wOtxYtxdjuPAhbDv2k//PZRgv6INhYDgK/7qRauvKLUjAmEA/JI+HvMceR+HnR8jdd7XomKMCjYjA2nIL1+lOLSbpLrVh+TMP2/4yPOV2BJWIKtoPw8Aw9P3DEBTNuxev+OILRKOR0HvvadH8jiEolQhqNV5rdav6y5wZZEuNjIxMm7Imw3dn/Nq1A9HrtfW26ZMSz4fX9uKiCInMapE7vs8h/YgvR4ogCPS/+BLs1RaKMg912LzPN6w7iyl4cTMV3x8EpR5VTBDOo0cx/5pJ/kubMf96FOmEZHnbzFaiNSqiNCoKn3se66ZNRL30EgqjsUXHDVApSTFo2V5V11dFkiSq1uRQ8OpWrLuK0XUPImBaV0wXxSOoRMq/zqDoPztwFTfPx8WVn0/AlVdgGD68RfMDkLxezB+9iORyIORsbHF/mTOHbKmRkZFpU0Z38zmRRpjqFzTHGNa/O8P6dycnt4BRb27hzW/WceekvvTqkURgVDRKlZr9f64mKrl10SsyDVO54ihVK7PQpYbgPyURZYAGyduHvIcfwfzj+wTOegrzSglnroXg63sgKEX2WGz00anJe+QRzN//QMQzT2NoJDdNY/Tx05FWdXxZR5IkKn84jGVdHsZR0ZgmxtfJzu43JgZnThVlX6RTPH8nIbf2QR3VuJhyFxaiDAtv9pwkScK69FMsP31N1ZaDuMwSfjF2/Hsbmu4sc9YgW2pkZGTalPAaMZNV1rw76pjoCCaFVLO0WM+ln2bw/Ic/odbq6H/ZDBavTuPrz97HUZbVnlM+r6ham0vVyixMF3ch6NruKAN8S0CCKBL14gsEXXc15e8/givnW+z7Syn+3wYs69eTUVhM8PffUfXrSqJeeaXZzsH1kaTXcuQEnxrLmlws6/IImJ5EwCWJ9ZabUcf4EXZXXxSBWko+3I3H7Gz0GF6HA9Ggb9Z8JEmi5OEbyPrrPCrXHcDQI5rYZ+8j+rJgRJVc+qYzIYsaGRmZNsVPq6JrqIEd2c2POnn7bzNZdltfoqnkgwNepjz5OXdtVrE0fDLTM/6K5s0+ULinHWd9fuDMrqLypyMYL4jGdGHsKdWnBYWC8L8/Qvxnn6LQVGHb8h7OLA+5z3xMgVJNfGgQid9/j/8lU09rHnFaNZVuD2a3B2dOFZXLjmAcE4NxWGSj/US9ipA5vQAo+yq9QUdiSZLA4wFF8wRJ1YcvU/LDNoLGJtJty24iP16BccbdCBojuGSfms6ELGpkZGTanNHdQlm8NZfPNhzF7fE2q0/3rjGseOpKro5x4/TAyDCJb2/pR7lYUwTz0yug/Gg7zvrcRvJIlC/OQBVlwP/iLo221Q8aRNx775L47Xy0KTqcw2fhFUW6XXUl6pjTr6geovZ5PhTbXZR/dxBVuAH/ifFN9PKh8FMTOCMZR0YFtl0NRDa53QB4q5sWJJXvPEPuKx/g192P8Ld+qFu/Sm0Ap5ynpjMh+9TIyMi0OQ+OT8Zsd/Hkkt18tC6Tf1zai1E1vjaNoddpeOG+K+psS9MmYHWbiFdJ8Ol0uPkXMIa108zPXaw7inAVVBN2T79mRxApjAaCZvbl4H+2Ar48M22Bf804JRllaHIshN7ep9lzAtClBKHtHoR5xVF0fUIQxLoWJ1eBrxRD0Qsv4n/JJShD6v/slT45h6KvNuLfL4TI95fCyQkEVXpwyaKmMyFbamRkZNocf72KV6/uxw/3jiLIoGbWBxv5dW/LKzRbrRb62DZjV/jBrG/BWQ2fXQF2uap3S5AkiapV2Wh7BqOO9WtRX1GvQjnY53Crsjecx6YllLl842zaV4Q6wR9NYkCLxzCNj8NdYsO+t/SUferYWBJ//AFlaChHr78Bd/mpS6GV7z5P0dcbCLowmcjP/0Aw+J96ELXe95mT6TTIokZGRqbd6B3tz6LbhjEuJYzHvk3D6W7eUtQx9C/5ljr0Q2ZBUIJP2FRkwQtxsPWjdpjxuYnjUCXuYht+o6Ja1d/Qx2fpsO0va5P5HLvwCKV2/Ea2bk7qGD/UcX5YNtVfIFOTlET855/hqaoi94EHkVx1602VfLgAfbyesDe/QmjI90ZlkEVNJ0MWNTIyMu2KKAo8OqU7RVUOftyV16oxYsfd5nsS3ssnbAB+nwfetrEcnOtYdxShCNaiTqjHGtEMtHpfAsSqg60rOXAy/jURRSlO0HYPavU4+gHhOA6W46muv0CmOi6OmDdex7p1K4UvvFi73XVoD84yD4HTpyKoGknuqJaXnzobsqiRkZFpd5LC/BiVFMKCjS0Lzd4eOxuADe/ci+StsfJED4RbV4KlEL6cDe7GQ3vPdySvhH1fKfreIadEOzWXY7405VVO3KWnXzagsmb5KTTeH0HZ+suQrlcweMGe3rAFST94MGF/+Qvln39O1W+/A2BZ8jEgoZ9yXeMHUMnLT50NWdTIyMh0CJf1jWLL0XIqrM0XIREX3gHAsPxPkTzu4ztiBsGUl2H/j5C5BgCX18UX+7/g0TWP4vTIQucYroJqvNVuNMmBrR4jUKVAAZRqBeyHKk57ToVVdgCiEls/J/BFQqkiDTgONe5j5Th0EJRK1F26AFD6xY/o47Qo45pI7Kg2yJaaToYc/SQjI9MhDIj3XcA2HC5lUu/G85EcQ2XwLU1sC5zEgJOXCQbOgQ1vwaLryA7vzj1GiSNO3/LIrJ6z6Bncs83m3plxHjWDQkATZ2qwjdvtRhRFxJOjf2oQBYEYrZqCUAlnphmGNO//1xCHi6rwd3oJ6hJwWuMAqLuYcDQhtKr/WE3AVTPQJCYA4LV7MYzr3YzB5ZDuzoYsamRkZDqEpDAjPSNNfPBnJgPiAglroowCwIFvn0MvaYi49Knabau3vsWWo78TY4xi0mVvYFr7Jq+Yt3PEqWdszFi2Fm6li6lLO55J58KZa0EVrkdQ1RUspaWlrF+/nvT0dKqqqhAEgfDwcPr06cPgwYNRn1RINFGvIdPfg3OP5bTndLDKTpwdFIGnFrRsKeooI9Ub8vE6PfVmIgbQDxtGxcJFeMrKMV08EQQBTrT8NYRK70u+J0m+PjJnPbKokZGR6TD+MjGZexZsY8jzKxncJZDL+8cwvkdYgwJHspaTrYwjJdFndfF63Dyy6y0sogBV+5mfs5KRqiB+M+iZaejKmvIDTOwyEb2qeenxzwfcxTZUYcffD0mS2LhxIytWrECr1ZKamkp4eDgul4ujR4+ycuVKNm/ezFVXXUV09PFEe72NOr4sq8ZdYkPySqfkhmkJezwuBnrEVvv4nIgyVAcSeMrsiBH112kKvvUWBI0a56HD5D70F1+/5tSFUhtA8oLbAaqmRbjMmUcWNTIyMh3GRT3C+fORcfxxoJjvduTxxHdpPPYtdA01MCQhmL4x/oQYNShEAbPdhau8ipGKDPC4QKGipOwAFlHg6aChDB14J/NWzmWJt5w4t4dxQx/ki9/u5bKul53p0zyrcJfb0SQFAD5Bs2LFCtatW8ewYcO46KKLUKlUtW0HDx5MaWkp33zzDR9++CHXXXcdiYmJAAzyN/BvishVQ4TZWVszqqWUOt0cUkrcIqiabtwMlEE+seEus6NqQNRoU1KIevZZkCSKbx+DeVcxfrc80fTgx8SxyyqLmk6CLGpkZGQ6lGCjhisGxHDFgBiKquxsPlLO2oPFbMksY+GmutFRtyliQAEsmAlXfURIUDIGr5dCt4XoqEH8+/q1LF/zT5LjLmCN+Sg6pY7+Yf3PzImdhUheCa/FicLPt5S0efNm1q1bx6RJkxg2bFi9fYKDg5kzZw6LFi1i0aJF3H777YSEhDA8wIgC2BCspL/FCa0UNWvKqwAYoT79pScA0eATR94GwrprkST47VlCo3cS8tDnCIGhTQ+urhE1zmrQtz70XKbjkEWNjIzMGSPMT8vU1EimpvocT61ON2abG68k4a9ToVNNgUNXwNe3wPsT2Bzdm2pR5IjHF2YriCIXj/H523y56UXC9eFtsqRxriA5PeAFUa+kqKiIX375haFDhzYoaI6hUqm4+uqr+d///sfXX3/NbbfdhkmpYLhRz2/hbu6wNcMfpQF+LK6gp8VLlK5tLDWCQkRQK/Dam5jTkdWw5hUY9SBC92YW5FTVWH7kCKhOgxzSLSMjc9agVyuJ8NcSFaDDoFEiigJ0mwC3/gpqI+L+HwCI0Jx615xjySHGL6ajp3xWI7lqcvsoBJYuXUpAQAATJkxoVl+NRsMVV1xBQUEBW7ZsAeDSABObgxUczW1dmYpSp5sVpWYmFHlOcVw+HSSnB+vWJspwbJgPgQlw0VPNd/qttdScvnO0TMcgixoZGZmzn9BkuG0lg27fyFhVMF9XHSCnKqdOk2pXNSZ1w2HL5yWS709mUTZHjx7l4osvRqlsvoE+Ojqa/v37s3r1apxOJ9NMfug88KmtdQnpPs8vRZLgsqLWW3oapKkkflnrwRTVsiimY5YaOay70yCLGhkZmU6DEJTAczO+x6Tx5/7f76fK6fPPsFnL2F64DZ1Sd4ZneJah8F3AN+3bRlRUFN26dWvxEKNHj8ZqtbJr1y6MWiVXZTlZqHNT7GzCh+UkzG4Pb2cXcW1kEIFe4bgVqQ0Q1Ar0fRup3G7OA0cVpExp2cDqExyFZToFsqiRkZHpVJjUJv497t/kWnIZsXAEY74Yw+PfzcAteUg/8itUtbwa+LmKqFFgFmxkFmUzZMiQVvkbBQUF0a1bN7Zu3Ypkd3NDphOVIPDMoZbV8XrpSD52r8T98eGIOiXe0/DLORHJ7UVyehB1DRSlBDi4EiQP9L6iZYOrj1lq5FIJnQVZ1MjIyHQ6ugV24+tLv6ZXcC/K7GWscBUDcGPeIXj3Qnm5oAZBKXJEU4xSVNCzZ+szLPfr14/8/HxK8osJcMHjkaF8VVDOd4XNK3C5stTMezklPNwlgiitGtGgxmNpmaWnIY5FPR2LgqqX3V+DMdy3/NQSZEfhTocsamRkZDolMX4xLLpkESsmL+Lh0nKWh13MpFkrwFIEf7zY9ADnCdmKEuL9ok7JENwSkpKSUCgUHDhwAIDr4kO5PCyAB/ZnsbYmRLshtlVWc8eeTMYHm7g91hdGrQzU4Cm3t3o+J+Iu841zLF/NKZRnwuFVcOFjLR9coQSFWhbJnYgWiZr58+eTmpqKyWTCZDIxfPhwfv7559r9hYWFzJkzh6ioKPR6PZMmTSIjI6PJcRcvXkzPnj3RaDT07NmTb7/99pQ2b731FgkJCWi1WgYOHMiaNWtaMnUZGZlzlAi/aGZVO4kMSICI3jDucdZvnc/mTf8501M74zidToo8FUQLwac1jlqtJi4ujqP52YgmNQqNktd7xDHM38i1Ow/zTnYRbq9Up49Hkvg0r4Qrdxykp1HHOz3jEWuWv5QhOlxFNiRJqu9wLcJVbAWhAVHz/Vx4oy8ICuh2cesOcKxUgkynoEWiJiYmhhdeeIEtW7awZcsWxo0bx7Rp09izZw+SJDF9+nQOHz7MkiVL2L59O/Hx8YwfP57q6oY/EOvXr2fmzJnMmjWLnTt3MmvWLK6++mo2btxY2+aLL77ggQce4PHHH2f79u2MHj2ayZMnk5WV1eC4MjIy5wnr/wuCCL2vpCJ9E0cqDTwREcXN+95he9H2Mz27M0p+fj5eJEIrdactIGJjY8mvLEIV4XOe1Ygin6QmMCc6mH8czGPohr08diCHfx8t5MmMHEZu3Mff0nO4IjyQL/p2xaA87vOiijYi2d14yk7fWuPKtaAM0yOoTvKpkSTY9rHv+eVvg6mVRTjlopadCkE6zU96UFAQL7/8MqNHjyYlJYXdu3fTq1cvADweD2FhYbz44ovceuut9fafOXMmZrO5jsVn0qRJBAYGsnDhQgCGDh3KgAEDmD9/fm2bHj16MH36dObNm9fsuZrNZvz9/amsrMRkkkM/ZWQ6PR43/DMYEi+E2d/xr5mXAFB8iZWl3mJ0Ci3/Hf8WgyMGt2jYlfsKCfXTkBoT0A6T7jg2btzI8l+Wc6P1AiL/MhhVaOtrYu3bu48vvvyC2wbNJPr/27vv+Cqr+4Hjn7tHbm723oxAAgRkb3Ag4kLrnlhtrfKzrdjaCq0dLqyrqK3WgatawYXiQkABmTIDYc8QCNnrZt39/P64EIzZi6zv+/W6r5vce57zfJ9cQ76e55zzvTyl1nt7Kqp5K7uQ9SUVFLpcBOu0jA2wMCsmlPOsdc/pqXSR8+gmgq5Lxm9EM2owNSL3n9vQx/kTfG1y7TeOfAf/vRqGz4IrX2j9CV4cCcnTYfrjbYpTtE1z/363ek6Nx+Nh0aJFVFZWMm7cOBwOBwBG49khQI1Gg16vZ926dQ32s3HjRi6++OJar02fPp0NGzYAvuHTbdu21Wlz8cUX17RpiMPhwGaz1XoIIXoQd7XvufQ4Bdu/rXm5co+dZEXHkLA07l5+N58c+qRZ3RVklbPuw4PMeXMbV724nrLq9pn30VmKiooIDg5CrVbjOFzapr4CvL7kpCq47lLsQRYTTw+IY8PYFA5OSmPT2FQWpMTXm9AAaPx06KL9sB9q3kTjhnhsDtx5VRhP17aqUV0Ci2+DpMlw6TNtOgd6s2y+1420OKnJyMjAYrFgMBi45557WLJkCampqQwcOJCEhATmzp1LSUkJTqeTJ598ktzcXHJychrsLzc3l4iI2pl6REQEubm5ABQWFuLxeBpt05D58+cTEBBQ84iLi2vp5QohujKDP8SMAEc52z58DX+9i9A+amKPB3Jf/1v5z7T/cHX/q/nrhr/y7HfPYrc3nKR4vQofPLGFnd+e5Fc2Iz9TTjHxg1E8sPqBdpn70RnKysoIDArCkBRA9d6iNvVlOOlbZVRhcLZHaBhTQrDvK0Zxt36/murdRaBWYUwOqv3GugW+RGTmS6Bt/QRpwLcCSm4/dRstTmoGDBhAeno6mzZt4t5772XWrFns3bsXnU7Hxx9/zMGDBwkODsZsNrN69WpmzJiBRtPI/gFQZ+8ERVHqvNacNj81d+5cysrKah4nTpxowZUKIbqFiQ9QUVrGvqM2ho8cwPZoA1qvmm8WryDj4GYeHvswswfNpvz7cp588kmys7Pr7Wb7ssxa35epfX/EVxxfwQs72nD7ohNVVlbi5+eHaUgYjiOleGyOVvWjeBWcu0rQqbVUO6rbJTbz0DAUh4fqPa1Ptiq35WFMDkJt/tFy7pLjsH4BjPoFBLbD/8jqzbKkuxtpcVKj1+vp168fI0eOZP78+QwdOpTnn38egBEjRpCenk5paSk5OTksW7aMoqIikpKSGuwvMjKyzohLfn5+zchMaGgoGo2m0TYNMRgMNSu1zjyEED1Mn6m8dngUXtQkXX438257npIoFWF5Glb9dT7vLF/IiW99/0Pjb7ORecONlKXvrNPND0uP1XxtMGv558zh6FwKlioF7TMLWTL7T3yweu85u6z2YLfbMRqNmIeFodKoqfih8dHtBvs5VIKn2I7JZGp0tKsldOFm9IlWKjacatVImOO4DVd2BX6jI2u/sflVMFhhyh/bJU7fRGFZ/dRdtHmfGkVRaubTnBEQEEBYWBiHDh1i69atzJw5s8Hjx40bx4oVK2q9tnz5csaPHw/4kqgRI0bUabNixYqaNkKIXsxgwXv6n7K35j2Ev8rMYws+p++smShqNUc3HMetdpMyLYVxefkEFxZS8Ic/4KnwzZP4/ZrfM+TtIRxL8hVtdBqquO2xcVjGjeWOzWb+9bKH1P3+zLGO5w/LjrFuV/dZdel2u9FqtaiNWvxGRVKx4VTT1ax/QlEUyr87gT7OH1tlORs3bmy3+PynxuE8bsNxqLTFMdlWHkcbYcY48EfFTSuLYOubkDgRLI2UTWgJnZ+M1HQjLUpq5s2bx9q1a8nMzCQjI4M//elPrF69mltuuQWADz/8kNWrV9cs6542bRpXXXVVrUm+t99+O3Pnzq35/re//S3Lly/nH//4B/v37+cf//gHK1eu5P77769p88ADD/D666/zxhtvsG/fPubMmUNWVhb33HNPGy9fCNETzHn/M+IGpQHw5QtPsf3rpUwbfy15ETpUKg3Xz7yeiTuLCNq2DQBXVhYF/1wAgFbtK/C4Nvgzdkespc+dKgynb2fc86/veXD8HH5+8dmN2woffRbF2351izqSSqWqGQXxnxILbi+2lS1Lyqp3FeI8bsM6LQEAs7n1K6h+yjggCH2CldIvjrRobo19TxGOQ6UEXJyASv2jaQjOct+eMsF92i1G30RhSWq6i+aXa8W3ud5tt91GTk4OAQEBpKWlsWzZsppS9jk5OTzwwAPk5eURFRXF7bffzsMPP1yrj6ysLNTqs7nU+PHjWbRoEX/+8595+OGH6du3L4sXL2bMmDE1bW644QaKiop45JFHyMnJYfDgwXz11VckJCS05dqFED2EWq3h+r88wf4N37Pxw/+x6q1XWfXWq1jj+uMBvvjfF8RUVzPxdHubzkxe/CBybFWEnkjisnWRrBydT/h0LzOHXlbTr15v5P4bxvGbb3yJwK/KDGSHzGxZpedOpNVqcbt9IzOaAAPWi+IpW5aJKTUYQ5/AJo/32ByULj2MaVAIxuQgrFYrQ4cObbf4VCoVgVf1I/9fOyj76hiBV/Zt8hh3mYOSJYcwpgRjTP3JpoJZp/c3S2347kCLyeZ73UqLkpqFCxc2+v5vfvMbfvOb3zTaZvXq1XVeu/baa7n22msbPW727NnMnj27yRiFEL3XwPGTGTh+MhXFRexZ8y0FzgqKPCoC/YKpLi6Gz5YCsOC+f1Fc4mLntoMQNQZuHsOojffwx1F152Fcef4Qji78mJ0h47Eqvv8hc1S6MVoaqTXURRiNxlpzYCyTYrEfKKHovf2E3ZPW6L41Xrubwrf3gkZN4NX9AKiursZkat9K6PooPwIv70PpZ0fQBBrwnxzbYFtPhZPCN3aj0moIuqZ/3cUi/qfnWSrtOJImm+91Ky1KaoQQojuwBIcw5urr67w+e9VJAmxF+Jt0bImo/c/f3Rc9hllX/x/5X7/1MP+5bw05QRpevzgAT1Y+42ICGRXg1yHxtxc/Pz8qKs7usaJSqwi+JYWCV3ZR8J+dBN+UUnePF3ylB4rf24+71E7Y3WloLHocDgcul6tdbz+dYRkXjcfmpOyrY7gLqwm4NAm1sfbn4zhaRvFHB1GcnpqY6tBbfM+2+le4tYpOVj91J5LUCCF6jWden8uYJ77FebAA+6BACPCNtlwU7M8VQ4c1eJxGq+H2J8bz6zd8c3KeyCuAvAJOTBmKTt11b0UFBgbWFKE8Q+OnI+xXaRT/bx+Fr2dgTAnGPDQMTZARb4UL+4FiKrfnoQkwEH7PUHSRvsStpMS3UV5QUFCd87SHgOmJaIIMlH1xlKpdBZgGhaILN+N1enAcKcWZaUOfYCX4xgFogxooXpl5eqPX1tZ5qo/e7Fv9pCjd5rZjbyZJjRCi1zDrtVx9XgxvbzqOO9J3GyVSr+XNIU1PLPUPNnLziFiW4fvj3s99iHfnVjDikkTSzm/4lklnCgkJobS0FJfLhU539naZxk9H6F1DqNqRT8W6bIoXHTj7XqAB6wXxWCbGoNaf3WOssLCwps+OYhkZgSnjQSqOh2M/cBHVuwNR6dXoY/wJvnkgpsGhtScG/1huBqz8K8SMBIOl/YLS+YHiAbcDdA0kU6LLkKRGCNGrXDIokrc3Hq/5funw/s0ebZk2NZ4rVr2BGg8XHsuloOxm1i4+yG/9Kvh1fDg/iwxuupNzKCoqCq/XS35+PjExMbXeU6lV+I2IwG9EBJ5KF95yJyqTFo1VX+/Gpjk5OVitVvz8OvCW247/ojnxOQFqCHAv9I2MRI2DgTMh6Wpo7HM6le57ThjXvjHpT99uc1VJUtMNtHmfGiGE6E7G9Q1hYr8Q9N/n8if/QOJNhmYfq1KpeGnSA1zj/IqC7TeffePgPnbOu4+vvvy8AyJuvcjISLRaLcePH2+0ncZPhy7SD22AocGd2o8fP05sbAePSGV8CLGj4Y/HfSUOLpkPxgBY/md4bqCvQGX6/8BeTx2/gNOxmdt5JEl/OomTDfi6BRmpEUL0KiqVipduGcHGI4VMHxTZ9AE/odMFYKx4AXCi9tfgsVVz/srF+FVXUL59E1x2RfsH3UouVw4DUwrIz3+N48d3Y7UOIzBwJCpVy/5/trq6mlOnTrXrcu46KgogayNc8iSYAuE83/5njL0Xqoph31LI+Ag+nQ2a+2HAJTDkOug3zTeCUnW63EL0ee0bl+50UiOThbsFSWqEEL1OgEnHJYOjWn385Gsn4nUeYPeak7irVmF0V2ONjMZk7hqroeyOXA4dfIz8gmUEBalwOIwcPbYVr7cKszmJ/v3+RGjo+c3u78CBA3i9XgYMGNBxQRceAK8b+kyt+545GEbc4XuUZcOeT2DXB7D4VjAEQL8LQHV6/o85tH3jOnP7SUZqugW5/SSEEK0w6fr+OGwv43HuQT96Kq6CfNiVgf3AwaYP7kBlZdvZvPlySsu2kTLwCUYMX8uWzddiMr7KiOGLMRqi2bnrFxw5+lyzay6lp6eTkJDQsfXzTm4Fta7p8gYBMTD+13DPWvi/zTDmV74ilrs/8r1vL23fuGSkpluRpEYIIVpBo1Uz+d77AfBs+ha3w0FYxj5KFr3faTGVl+9hR/rP8TP3Y+yYr4iOvp7AwEgGDhzIli1bsVqHM2zY2/Tt+wcyM//NsWNNVx/Pzc0lMzOTkSNHdmzwJzaDNdo3h6a5wgbABX+Cu1fB7w7CDe9C3Nj2jatmpEaSmu5AkhohhGilUZMnkuDR4R/Yl1EJNxMWPYXyI5kNH6AoYMuBvL1QWdiusbhcNnZlzMZsTmTo0IXodGf3k5kwYQJFRUXs3r0blUpFYsKv6NvndxzLfIGCwm8b7Xf16tUEBgaSmprarvHWEZ4CZSd9P6PW8I+AlCtA086zKs5syCilEroFSWqEEKINrvrfYi4Nuhb/pOMU3ZzL/ebzqHT8pBK2yw7rn4cXhvlW8bw8Dp7uCy+Ng21vgdfT5jiOHHkKl6uUIYP/jVZbe25PbGwsAwcOZMWKFTVlExIS7iU09EL27/8Tbnd5vX0ePnyY/fv3c8EFF6DRaOpt024CYn37wRQf7djztFTN6icZqekOJKkRQog20Or1hP3mPPIGvUlh5G4OBsVTbv9RUlN0BF6ZDN8+CgkTfLdI7lwO174BIf3g8/vhzUuhIr/VMVRUHCD71CL69pmDyVT/sutLLrkEp9PJ559/jqIoqFQqBiT/HY+nkszj/6nTvry8nE8//ZQ+ffowZMiQVsfWbGnXg8YAuz/p+HO1hEbnm+sjc2q6BUlqhBCijQzRFhZUPsZdqvfQ+/sRYtGzraySF7auxfbKBWTb7Tw7/QPWTP4HysDLIX4MDL4Gbvgv/PxrKMmEhRdDeV6rzp+Z+RJGYwwxMTc32CYwMJCZM2eyZ88evvnmGxRFwWiMIjb2dk6efAeXq7SmbWVlJe+99x4AV199dYN717QrvZ/v9tHezzr+XC2lN4Ozoul2otNJUiOEEG30ux372WJJASDx/Dju2pPJjZt38LNv76bcGMyzF7zDR0okN+w8wswdhzla5Th7cMI4uGs5uO2w6CZwO1t0boejgPyCZcTH/Ry1up4ijz+SmprKjBkz2LRpE4sXL6a8vJy4uJ/j9brIyV0CQGZmJq+//jo2m41bb70Vf3//5gfjcftWIjV1DV6Pr6zBltchfx9Ul8LepeBxgL2s+ec7V/QWuf3UTcg+NUII0QZ2u52du/dAbF8AdjscBHjdfFu4kBhvBaqff80DeZXsWv8VR/btpaKinIV+/gweNpzpV1xNcHQMBCXADe/BGxfD90/7VvQ0U17eUlQqNZGRP2tW+zFjxmC1Wvn8889ZsGABycnJhIYN5eDBt/lmmZvs7GxiY2O57bbbCA5uQdmHw9/CZ/8H5TngFwa/WAlBifW33fCir05Tffpf3PxznitSqbvbkKRGCCFaSVEUlj22iL97rfwhwMMRf99k2lWRJYSu+ATnxc+y/J0POLDhe4KjYxkydjxqP3+WHjjCsfXrePO75VgjJhIcez5h8QEMSPkLEesfg2E3Q3BSs2LIL1hGcPBkdLrm7yGTkpJCYmIi27dv58CBA9jKg+nTZysBgU4mTLiegQMHola3YCD/yHfw/k2QOAEu/6dvntB3j8M1rzXcPijRl8jlZsAXc3w7BE+cA+EdvMqqNc5U6hZdniQ1QgjRSlVb8hjmTqRQVU6Q04tKUdPXz0jouqeoDh7Ch5/upCw/jxn3/Y6UiVNRqVScOlxK2o692EwjKbdsx5a3FpWqlPKSGWSUDKGv3x+Y+u0LGK/7Z5Pnd7vLKStLZ+CAR1scu8lkYsKECUyYMAG3+3q+X/sd48cHERvTwqTi6BpfQtNnim8StNYAtlPw5e9g4v0QMehsW48b8nbDsTVw4V8gcrDvkXK57xbPuZi70xo6Pxmp6SZkTo0QQrTCoYwtvJt+P/81ruK5uHy2hui43aHjzbBKPEfX8lnWACqKi7jpkacYMGEqp8rsrF+VyZLntlGoymXr1M95e8oujlzcH1v+buL67WHanalku4fy0eoRlJ/IbjKG0rJtgJegoLZtOKfVWrD6D6G05IeWHXhyK/zvBt+qruv/60toAM67zXdL7dsfJVu7PoBHQ+DVKb4EJvWqs+8Z/LtuQgMyUtONSFIjhBAt5HKUklVwI/36baFPynp+6JNCn/xsHh4SQ+K+xWwqSyHnVCHjfvUgz2yxcd4jy7nlqcVsW3yIg8Fb+ajPW5hCdCSYLXyv38iGlALSl3+By3uAa38/BC9aPv/XLhzV7kbjKLftRqsNwGRKaPM1+VvTsJXvbv4BpSd8IzRRaXDje76ikmdo9TDlITj4NRScLhux9c2z79+fASF92xzzOSNzaroNSWqEEKKFin/4MwAqr4G9oX2p0ht5JC0GS3QARdu+YnNuCNaxM7h5aR7f7s/n/NRTXG4LoiTkFCETh6MrnMOK1Rdxsfn3VMa9iPX82zkV4eCzV57BYYUrR6yhslxhzXv7G42jsvIQFr/kdlly7W8ZSHX1cTweR9ONvV746E5fInPDe6Az1W1zZpKwLdtXZfvkZjj/z3D3Gl+Byu5E7yern7oJSWqEEKIFylYvovCLXPzL3fi7VHyvv52hbKefaRPk7mTDcT80Zj+eOBnFZWlRPHmLATLyMKgN3P/gtfzlkmmsmDOFuyf34cUVh0g+4WWraiI3zv4bumov81+5D9Ow8Uz2f5lDW/M5vqeowViqq7Mwmds+SgNgMsUDCnb7yaYb7//cl6Rc9TJYwupvU3LM96zRw8FvfMu4R8yC6GHtEu85pTNLmYRuQpIaIYRoJrfLw9JPDay2zaZ831QOGII45PbjAs0moqKuoTT9Gw6Wh7LSeB53TunPHy6NYsHK50kuGM2Ua1IJCPRtua/XqvnDJQP5+5WDOLCngKKjNqrC04gfP5qQ3VU8a9tPsvF7omPcbFxyBMVbfz0khzMfgyGyXa7NYIio6bNJO96D2FGQOLH+9xUF1vwD+pwPCePBdtI3OtNUBe6uSubUdBuS1AghRDMtfux7bI4AEpP1HM66joPlwzHi4lejHsNgCCN941Y0aoXQ4ZOYOyOFZ7Y9Q8qJifgF6Rk0sW75glnjE7l1bDy6/WUsPpzHBTNvx+TQsP6HFewKiWdM310UnaxocLTG5SpB/6PClW2h04XU9Nmoinw4vBKG3thwm8oC3y7JI+/0TQAuzQJL+yRfnUI23+s2JKkRQohm2PXtWnL3/xOn7WPy8zyYrH7st1zGhEArAeZIFEXhQGY5equeJ28Ywf6SfXx3cA0JhUMYen48Gk39/9z++bJU/Mw6vl2XRVhCEmGJfUgrjGRBoB9RrnWExfuz5/u6K6EUxYvX60CjMbfL9Wk0vnkx9uoTjTdMfw9UahjUjM3+NrwIn90H29+Bvue3Q5SdRCYKdxuS1AghRBMqThSw4tV/AF687ixKsz9hxMxktuqiGBscCMDuXXupcOqIH5hMuL+R1zNeZ2TV+ai8KgaMbXiUwqjTcN2URBz51Xx7uIDkMRMIz9WwzVNJuu0IKeOjyNpTjL3SVes4RfGtjFKp2qd6tkrl27YsP39Zw43sZb5q4+fd2vhkX0s4XPqMb4n3ga9BrYVht7RLnJ3izO0npf7bgKLrkKRGCCEaoSgKJ6+4koAqOwBqjQav6wiZVYeo8ngZ5u8bKVn59TI0Ki9Tz59AQVUB32V9x3mVU4hODsRsbbwm023D4/D6aXn5+yMkpp2H1+liUEUwH6ir6DMsFK+icDyjsNYxZ5IQr9L4su/mX6cvaYqMvKr+Bl4PLLnX9zzlD013OPqXcMcX8Nt0+PU2iOiCOwU3l84PFA94WlaXS5x7ktQIIUQjbJ9/jmIvZfyhbIZYRhEcGg3AidOrqAf4GXF5vBQcOUCksRxLWCzLjy9Ho2ghx0R8akiT50gyG1DFW9h2sBAMVrR6PROciXxrNqLWlBAaa+Hk/tpzXVQqNWq1EY+nfW6LeDzVAOgNpyfzej3gOT065KqGT2f79p352atgjW5+xwb/hmtAdRf607f4ZLJwlydlEoQQogGu7GxyH/HtiqvSGUkJPZ8p/zcMQ7yVV07kYzqaQ5hey9pDhZirSwi3VIDByrdZ3zLFNB2300t0/8Amz+MtLub+bZ8x8vvvKfm0DP9+0ZhWlnCnn8KWmE+I7jeN47vrThbW6YJwOYvb51rXPgGAfuV8yP+jb38ZxQvWGF9yU10CP3sNBsxol/N1KzrfqjXfvJputsdOLyNJjRBC1EPxejn10FzU/v6odDo8JSWEzkrAEO8rHFngdBOu16FSqfhubw4Wl50QQxXViof0/HTuM1yOXa0iNNbS6HnKV68m548PMcXlZk2/Ubj6DSZVn0Xhsf0MOZBL4Jx/U317KGUFYTir3ehNZ//ZNhgicDhy236xLjv2jDdhiD8GvwQYNBUC431zYUqO+/6Yj/g5hA9s+7m6o5qRGpks3NVJUiOEEPUofvMtqrZuJf6tt7B98QWVP/yAMSW+5v0Sl5sgne+f0B37jjMZhQCdnT2lh3B5XYQ5YykL9aDVNzyRt+yLLzn1hz9gmTKFF269m/RjDsoOlfLKkBCysg7zzS124vZEkvbRGzDij5TkVRKRGFBzvNmUQFX18bZfbOEBqg0AKoxXLAR143OAeh3d6aRGNuDr8mROjRBC/IQzM5OCBQsI/vnP8RszGsfRo5iGDKlVjsDhVTCqVVQ53eTmFwBg1rrYX3IQg8aAutxAQGg95QNOq9qxg1MPPUTAlVcS+68XWe5Vc9SoUO5wU6UxUW13MNDl5OVJDmLuuRWAnE9X1urDzy+ZiooDKIq3bRect5dyixazKQm1JDR16U+PtslITZcnSY0QQvyEff9+FJeLkLvuBMB57Bj6Pkm12ij49pXbn1uO/vSqGKPGzbHSoyRaE7GXuxtc9eStquLU736PacgQoh59BJVGg83txXu6fZ5DDQok2L24vC5c10xEhZeCL7/FceRITT/+1sF4PBVUVR1t2wXn78EW6IfVmta2fnoqmSjcbUhSI4QQP+G1+5Zva/z98TqdeIqL0cXE1GqjU6lweRWOF1WiweNrr/JysjKbWP9YHFUuDH66evsvfOVV3EVFRD/1D1Q6X5uZ4YFMDLMS7Ken4EQWAEFu3wjByYqTmKwGPMER5D72WE0/gQHDUam0lJRsatP1uvJ3UW7yEhQ0pk399Fhy+6nbkKRGCCF+QrHbQaMBnY6qgtP7w7hr7wdj0aop93g5VWrH/8y8GYOVQnsxoaZQPG4vWl3df2LdxcUUv/MOwbNmoY+Lq3m93O3BolUTG2TCVuWrlB3gFwVAkb2IKpuLkqSJVG3cROUPmwHQaMwEBIygsPDbNl1vkWMfqCA4uIFaTr2d/vTqJ7n91OW1KKl5+eWXSUtLw2q1YrVaGTduHF9//XXN+xUVFdx3333ExsZiMplISUnh5ZdfbrTPqVOnolKp6jwuu+yymjZ/+9vf6rwfGdmN64gIIbo0b7UdtdHI9udf4MSFF/peq6z9f+khOi2FTjeFFQ78zEYAPAF9KHNVEmAIQPECqp/2DKWLF4OiEPzzO2q9XuRyE6LTEuynp9QYCkBwdBImrYkyRxkAxaUqdMnJbH36X8z9JIP/bjqO2nQBxSUbcDobrubdqKpi8qzVWLVxGI0t2H+mN9HoQK2TUgndQIuSmtjYWJ588km2bt3K1q1bueCCC5g5cyZ79uwBYM6cOSxbtox3332Xffv2MWfOHH7961/z2WefNdjnJ598Qk5OTs1j9+7daDQarrvuulrtBg0aVKtdRkZGKy5XCCGa5szMxFtZifHVVwFQdArlylE8HntNm2iDniKXm9JqF3qz7/aEI2gALo8To8aIWqvC6669rb6iKJR++BHWSy9FG1S7EOUJu5MYox5/ow53dQUA+tihVLur2ZSzCb9AA0GjQllgGUrI7q2s37iXhz/dzV0fBKBSqcnJ+ahV12rP/p7CYD1R4Ve26vheQyp1dwstSmquuOIKLr30UpKTk0lOTubxxx/HYrGwaZPvfu7GjRuZNWsWU6dOJTExkbvvvpuhQ4eydevWBvsMDg4mMjKy5rFixQrMZnOdpEar1dZqFxYW1orLFUKIppUuXgyA2utbVVQ5ycvh+Hc5ePDvNW2STL5JvSVON4rRN/el0r8/XsWDylWF3qjFYa99y8q+axeuU6cImDmz9vlcbopdHpJMBj7feQpP7iGMGheapPEAbM/bic3m4JOMHNbFDEWt1fDxECfTB0UQag0nIuJKsk68hcfjaPG1Hs95D60HIpPubPGxvYrOT0ZquoFWz6nxeDwsWrSIyspKxo0bB8DEiRNZunQp2dnZKIrCqlWrOHjwINOnT292vwsXLuTGG2/Ez8+v1uuHDh0iOjqapKQkbrzxRo4ebeNsfyGEaMDh2fdyqH8/7FdcTumECeRO8QfA4SyoaZNq8S3Xtrk9OPT+qFRqbLpodIqCq/AAJn8d1bbatYLKV69GExiIeeSIWq/vrvCVKBhkMWHWa0jynMSqd0H0cPx1/miKL0LjBY9OxdpHr8QydizVq1cTF2TG7VVITLgXl6uIEyfeaNF1VlVlcsqVTnypP1pDYEt/TL2LjNR0Cy1OajIyMrBYLBgMBu655x6WLFlCaqqvUNkLL7xAamoqsbGx6PV6LrnkEl566SUmTmze5LPNmzeze/dufvGLX9R6fcyYMbzzzjt88803vPbaa+Tm5jJ+/HiKihq/h+xwOLDZbLUeQgjRlAPOQ5Re76FoegUl11ZjDitFpTLhdpfXtLFoNfQ3GyjxerF7VQSER1BcZMNPY6Aify+WYCPlxfZa/VZt+gHz2LGoNLU35NtWVoVFo6av2cD5A8IJ85QSFBqEV6Olyl2F1enbcO/J24bhb9ThN348Vdu3Y8CDw+XFbE4kLvYOjmX+i8rKw826RkXxsH//n9B7NMQzuI0/sV5AZ5aRmm6gxUnNgAEDSE9PZ9OmTdx7773MmjWLvXv3Ar6kZtOmTSxdupRt27bx7LPPMnv2bFauXNlErz4LFy5k8ODBjB49utbrM2bM4JprrmHIkCFcdNFFfPnllwC8/fbbjfY3f/58AgICah5xP1ppIIQQDRk58ggxMQdwun5ApcpAcQ1GUapxFBfUajchyJ88r4fSKhdhiUnkHztCkDmcYtsJAvzdlOVXo3h982oUlwv7nj2YzxtW53xrS8oZF2hBo1IRWJKBrVpD+MDh2Bw2PIqHMI+vKGZMnC+5MZ03DMVuR5uViaL4+u/T536Mxlh2ZfwfLldpk9d4+MjTlJT+QEqWBk1gnzb8tHoJvUVWP3UDLU5q9Ho9/fr1Y+TIkcyfP5+hQ4fy/PPPU11dzbx583juuee44oorSEtL47777uOGG27gmWeeabLfqqoqFi1aVGeUpj5+fn4MGTKEQ4cONdpu7ty5lJWV1TxOnDjR7OsUQvReHu8BAC6dsZ0Zl+wigmRUHj2hR39Wq920ECtlGsgrtxPZbwA5Rw4SGZxKjk5HWMV3uBweygp8t5acmZkoTieGlJRafRS73Gwsq+DCEF9NqbG5H+H0aokccymnKk8B4M0349ap8Av0zeMxDhgAKhXW7KOEWAyAb3l32pCXcbmK2b7jVuz2U/Vem9fr5tDh+WRlvUb/fvMIPpXrq/MkGqc3g7Ois6MQTWjzPjWKouBwOHC5XLhcLtTq2l1qNBq83qa38P7ggw9wOBzceuutTbZ1OBzs27ePqKioRtsZDIaa5ednHkII0ZSggHHotCE1oyAV7r1Y8kbgnz+yVrtJQRbMFh0OlxdrUgpuh4PYiiCOmyyEHX0RVJBzxLcc23ncV6PJkFR7Z+Kl+aUAXBoagDd3LxGlR1Gp1UQPGMRxm++YOFcgQbGWmjINarMZbWQkAcV5uL1nV1j5+fVh+Hnv4nKVsumHGWQefwW7PQcAj6eagoLlbN12DVlZb9C//5+Jt04DrxuCEtv3B9gTye2nbqFFBS3nzZvHjBkziIuLo7y8nEWLFrF69WqWLVuG1WplypQpPPjgg5hMJhISElizZg3vvPMOzz33XE0ft99+OzExMcyfP79W3wsXLuSqq64iJCSkznl///vfc8UVVxAfH09+fj6PPfYYNpuNWbNmtfKyhRCiYZaSoZSoN7L+iwvRq0OoMu0nOPdSFKenVju9Ws2MxBCWbivklC4ES3AIhiwnhQEOKgxuwizFnNxfTMr4KFynclAZDGhCQ2uOVxSFt7ILuTgkgHCdGvvSOewpj8TafwhavZ59xfswuMOIcWkZfF547XPHxGApLaDc7sLrVVCrfQmPxTKAMaO/5MjRZzh6dAFHjjyFWm3A6/WtjPLzG46/5Z/sTFdYX/Ax/iGjCDr5KSHezfj5xRMaMgWTSUZu6tD7QUV+Z0chmtCipCYvL4/bbruNnJwcAgICSEtLY9myZUybNg2ARYsWMXfuXG655RaKi4tJSEjg8ccf55577qnpIysrq85ozsGDB1m3bh3Lly+v97wnT57kpptuorCwkLCwMMaOHcumTZtISEho6fUKIUSTgvNn4D1lpCJtMy53IebCwfgVDkFxeVG8Cir12V31fpsay2efHuTdA7ncPm4iu7//DvV4SB89i4SlK8lID8TjTsFdXIQmOLhWUcxlhWXsr7QzPzkWvn+aymO7KLaPYOLkqQDszN9JXNEY1F5IHBJaK0ZNcDDRBSWcLKlmyjOr8Hohu9R3q8ti0OJVJmDSpNEv8DBXpemYMjCRgsIQvl25A5ttC/365xEeuw6Ntpoyey6lR/QYjZUc5O9EhF9G/+SHMehrn7NX05mlTEI30KKkZuHChY2+HxkZyZtvvtlom9WrV9d5LTk5uWaYtz6LFi1qVnxCCNEaistL5Y48TKkhqA0aHAdtRE24Esu4X+E8WUHRW74NRjWBhjrH9vUzEh5uZvWRQv509UVs+/IzRpTGscmg5d7hwWxdq+b4119jrqhEY7HUHOfwennkyCkmBVkYd+B/sHo+XzivxKmpZsTkKVS5qthVsIvJeZegDzYQHFV7mwu11R//vFyev3EYGSfLMOo07D5VRmZhJbeOTUClUqFWwfrDSfxlZT6/y1fI3beGsNg4po7Zj8fxLaHevvTdfRT37ev47rvv2LZ1G6PHQHHJcjZvvoJhQ1/H339Qx/7wuwu9WSYKdwMtSmqEEKInKl68n+rdRZQuPYJKq0ZxeTEPDaPov/twHrehCTYSMCMJ08DgWqM0Z1w3KIqXVh3hUZuXmcNHoRzczbKYVcy74Wsidi4hY8UpRlesR6U9mxQ9cSQHZ1kOr59YDHsWU552N3kfHMQ1aCpavZ6Vmd+hd5jpWxnOsOmxdc5Z9tHHAMwcFsPMYTF13j9jWmoEv31xL7n7jpLujmKk8Rsc1em8nnEHPyvaRqAlmITAQH72s58RHx/PF198wcSJD2ENeIcd6bMYOeJDzOakBvvvNWTzvW5BCloKIXo1V34V1bt9e15ZRvjjp/6CYPOLeL54AufxQowDggifPRTzkFBU9RSoBPhZWjR4FJbvz+fghEtQldoJ2VvN1oJ0hl43hZPOoRSVOSFvN/z3Zxz5351M+/IWNv9wAwGHl8HMl/j8UBAOlY7xV14NwJdHvmRqwXW4VHDIXHckW1PP/MP6qKpKGEom540cxROzNIyL3kZwzOP8buZs1EZ/NK6zK3pGjhzJtGnTWLcuA3/Lw+h0wezKmF2rPESvJSM13YIkNUKIXk0Xbkal12AKzsSy60YCPK+g8xzGcPxlIvT3EWR6Cc0P/4AvHoD5cZC3p04f/cL9SY2yklLi5Z8uA1VjpjLiUDAfbXibfiMiCIm1sCv6PjyGcI46vBTn7ifQEoz64sdgTgb7KmPI2b6B9KjJTB4UT3ZFNrsOHSD+5BAOBqo4Wlr3j6ll8mRMw4Y1em0ej4elS5cSERHBhVMHkZf9HLGxsxiVch1j+4QQGZtEkKf2Jqbjx4+nf//+fPXVGgYkP0dVVSaZmf9u08+4R5A5Nd2CJDVCiF4vZODnhFTdh5pKPLPWofvLNlxXL0MJSUVdtgd2LoKtC8Fhg1Pp9fZxy9h4jh0v5Q/hobwxZAplVguWpUdYvGstYZfHUeLyJ8N/MhP7/o3VV3/MoLs+RjX2Hk4cOc43/3mBI9YBjJ42Db1WzcL0N7jo8O1Ygo2s9FaTElV3OwpPWRmagIBGr2vXrl3k5eVx+eWXcyzzOXS6IPr2+V3N+9qAaPyw46k+u9u6SqXi0ksvpbq6ml27CklIuJvjWa/XLA3vtfQW3/J3t7PptqLTSFIjhOi1XK4y9uz9PdusvgKWxRMvR9vHtzmeftgo9L/9GNWv1sADe2DiA76DBl5Wb1/XDI8l1GLgeEYBy8cNpnTWg3jUag499zR/2rmWfEsep6Iu5G11IA8mRYGikP7Nl3z8xF9whyWyKux8fj4xif2F+ylZZiK4KopRNyXjUYFJr6lzPndhIZrg4Aavzev1snbtWlJSUggMdJCf/xVJifeh1Z6dcGwMjgagNL/2xqRBQUGMGDGCTZs2ERV5BxqNmeNZrzX/B9sT6X2V2GW0pmuTpEYI0Wvt3ft7Cgu/JTZtLnaDGnVhA7uUFx2BTS/B+N+AKbDeJkadht9dnMxn6acoyK3g3+NGMO7B/6PUUsV1X71DP9cajIWfsu/tz/h4/r9563ez+faNl4kfdz6vmy7grin90XvcfPDCBvoUDeOiO1NJSQ1laFwgf/50NyeKa9+Ccp04gS6u7gTiM44ePUpxcTHjxo0jO/s9dLogoqJq74jsH+orHVOWV3e39bFjx2K329m//xixMTeTk/Mxbncv/oOuO50MSlHLLk2SGiFEr1RevofCou8YkPx3EuJ/iSMkBl1+PcUgFQW+fAD8wmHqQ432ed2IOMYkBfPA4p0UlDu4JPUyAm6dzJqRxbjMaspVR3FVriBz52qqKwMYdvnvWWIbxgiDHyNKFN55eD1+xWEMnxXKgJFRqFQqnrh6MMWVTg7kni2m6S4uxlNSUmd34h/LyMggNDSUmJgocvOWEhX1M9Tq2kvSgyJ9SU1l0ck6xwcFBdGnTx8yMjKIjr4Bj6eCwsLm1fHrkc6M1Mhk4S5NkhohRK908uS7GI1xhIdfCoA3ajCm0hIUr6t2w4wP4ehquPw5366yjVCrVTx/43l4FYVZb2ymoNzB3DHziB6Wxr9TfuCizFxunTidW554ndi0m9i7wcvYLA/jcxR2bTzKntD1DJ5tZsLYYb4YS6q4f1E6eq2aUYlnbzXZ9/iKCBtTU+uNw+PxcODAAVJTUymzbcXlKiYivO5ts+CgEMoVE87S+utEpaamkpmZiaIEY7UOJb/gm0avv0fTye2n7kCSGiFEr1Ratp2QkCmo1b7tujTxk9F6FKpOrjrbqKoYls2FQVdD/2nN6jcywMg7d42moMLBzH+t4/uDxTx//vNc0mcG6RF2ti1/l/fzl/JPJZ3X4vZwckY6n457mg/GPM6td01jeuqFNX1tOlrMofwKJvULxf2jGnpVW7eiCQ5GFxdXbww5OTnY7Xb69+9PcfF6dLpg/P2H1GmnUasoUgWj2OqfBNyvXz8URSEzM5OQkPMpKdmAonjqbdvjnUloZaSmS5OkRgjRK3k8Fei0/jXfm/teCYDr2LKzjVb8BTwuuORJvF4XtvLdZGa+xMZN08jY/WtOnPwvTmdhnb5j/E7y8s/yCDfncdfbW5n+zw1UZ19Hab8LiDlaznu7nqLQ+ixKzKt8X/UxFwyczGdXf8ak2Em1+pk+KIJJ/UNZfbCAv3x2dil55fr1+I0dg0pd/z/hWVlZ6HQ6oqOjKS3dTFDQ2FrlGX6sXBeCtiqv3vcCAwMJCgri+PHjBAWNxe0up6LiQP0/0J6uZqRGkpquTHYUFkL0Sn7mvlRWHa35XmuJpdrPiHJyi++FzPWw4794L32a9EO/o6R009lj/ZKprDxEfv5XHDz4N4KDJ2E0xhAcNI5Tpz6guGQ9APcNhv3R/ckou4rMwlRKIi7iQs+3/MPzADGXj8esMxFjiUGjrru6CcDfqOOdO0cz89/ra0ZqnCezse/eTfDP72jw2nJycoiMjESthoqKfYSFNTzKVGUII8DecKHG6OhocnJysPpPAVSUl+/G37/+2149Ws2cGrn91JVJUiOE6JXMfv0oLl5X6zVHaBy6/GPgssMXc1BiR7JN9TW20l0ADBzwGJGRV6HRmACoqDxEVtbruN028vO/4tSpRej1YcTF3UlCwq/Q60IYnLeUIQcfQa3WMnDA41TsTKP/tg3E3/vzZsX5w7Fidp0sY860ZADKPvkEtdmM/9SpDR5TUFBAdHQ0dvspPJ4qLH4DGmzrMoVjqdrX4Pvh4eEcO3YMjcaMyZRAZWU9k6l7gzOrn2SkpkuTpEYI0Sv5mfuSnf0uXq+7Zl4N0cMxZy3G+81DqEuOkXvlHMqL32HE8MUEBo6s04fFrz+pKf8AQFEUXK5itForarWupk1k5EyCgsaz/8Cf2JXxKyInjUT9r3U4Dh/G0K9fk3Huy7Gh16qZmhyG126n5IMPsF55BWq/hictl5SUMHjwYKqrswAwmRIabKv4RxJcUOxb5VXPLaqgoCCqqqpwOByYTfFUVR9vMuYeSasHtU5Garo4mVMjhOiVtFp/FMWDx3P2/7z1faajVkC99U246G9k2dcRFjat3oTmp1QqFXp9SK2E5gyDIYy0Ia+QmvIUBSn78ASryX7ur82Ks6DcQbi/AZVKRcn/3sdTUkLIXXc12N7lcuFwOPD398fhyD99/sgG22sDojBjx1FVWu/7/v6+eUcVFRUYDJE4nQXNirtH0ptlpKaLk6RGCNErORy5AGg0xprXTH2uwKNRUR3Vl+NRaioqDxAZcWW7nE+lUhEVdQ1jJyzDc0Miju+2s2/xL3C7yxs9Tq1SUVbl4viBTAr//W8Cr78OfQOrngCqq6t912Iy4XKVoNGY0WgMDbY3hfg28CvJzar/fZOppl+dLgiXs6TReHs0nZ+sfuriJKkRQvRKdodvGfOPVy+pNHp2X3IZmwfC4aPPkJgwu9FJtq1hNEYz5NefozkvEc9z6/lh+XSKir5vsP2tYxMIM6nZ/sv7wGwm/P77G+3f7XYDoNPp8HrtqNXGRttbw07vKlxQdwM+8N1WAyguLsbpLKTaXn/y0yvozeCsaLqd6DSS1Agheh2bbRc5OR8RHDwJvT681nv+IaNwe8ow6CPo02dOh5xfrVaTtOBt9PoQAp+3s2vdz9m3b269k3AjTGpeO/kFfQsz2fnzB5ssYuk9vUpKpVLhVdyoVI1PnQyO8CU19qLset93OBwA5OfnU1C4oslr69F0cvupq5OkRgjRq7jdlezeMwezuS9pQ14+O0n4NKs1DYDwiEtRqTrun0hdRDjxC99AV+lH1LOhFH23hB9+uKLWPjCOQ4c4PusOXGtWsezq/+N9e8MFLM/QaHzLwz0eD2qVDkVxN9o+ICAQm2LGWVp/UuN3ekJy//79iYq6trmX1zPp5fZTVyern4QQvUaZbSd79tyPw1HA0LRXa5Zm/1hQ0HhiY28nKfH/OjweY3IySYsXcWruPEJe3IwrykveyMeoCBiIff8+qrduQ5cQT/zbb1N5Qkf1seIm+9Tr9QCnJwub8HgqURSlwc33VCoVxeogKM+t932n01nTr9OlbnQlVY+nM0uZhC5OkhohRI+lKApZWa+Sefw/6LSBuNwlGAyRjB71GX5+fes9RqMxMiC5eSuT2oMuJob4t9+ict06Drz+S9x7T1ClLkWXkEDU/PlYL7sUtV5P5ZGdmPX1b9L3YyaTCZVKRVVVFcHBwXi9DjyeKrTahpeAl+tC0VbVvwFfZaXvj7ifnx8lJSXodU2PFvVYerOM1HRxktQIIXqsY8ee51jmi8TE3IxG44fXY6dPn9+i0wV1dmi1qFQqLJMmYfOoCe97B3Fxd9Rpo9Oosbu8dQ/+CbVajb+/P2VlZfTtGwGAw5GDVtvwnjjVhnCs9vrrP5WVlaFWq/Hz88PuONXo8vAeT+cHlXXLYoiuQ5IaIUSP5PHYyTrxJvHxv6B/v7mdHU6zGI0x2Gy76n2voNxOiEXfrH6Cg4MpKirCZPKVM6iqPo6fX8NJjcscgX9FRr3vFRUVERgYiEajobrqONaItGbF0CPp/WTzvS5OJgoLIXqkwsJv8XgqiIm+qbNDabagoPGUV9QtWeD2eNl8rJjz4ps3whQREUFubi4GQyRarZWK8obLIACo/CMI9p7eVfgncnNziYiIwOUqw+44hcXScMmFHk823+vyJKkRQvRI+QXLsFgGYjYndnYozabT+uPx1B0J2J9bjs3uZnL/0Gb1Ex0dTXFxMVVVVVj9h2Cz7Wy0vTYwGpPKSaWt9sZ6Ho+HU6dOER0dXdOHv//gZl5NDySb73V5ktQIIXqc4uL15Od/TXT0jZ0dSotoNJZaZRvOOFni2yU4MbThyb4/lpSUBMCxY8cIDBxNSelmvN6Gl3bX7CqcV3tjvezsbJxOJ0lJSZSUbESvD8Vs7tOsGHokvVluP3VxktQIIXoUj6eKvfv+QFDQWGJjbunscFpEq7XgdpfXSUDCrb4yB7ll9mb1Y7VaiYiI4MCBA4SETMbjqaC0dHPD7U/vKmwrqJ3UHDhwALPZTFRUFAWF3xEcPKnBpeG9gizp7vIkqRFC9CgFhd/icOQyIPnvHbp5XsdQoSjuOregogN8++nkNDOpAUhNTeXAgQPo9ckYjTHk5X/RYNuQyHgAqn+0q7DX62X37t2kpKRQVX2IqqrDhIfPaMnF9Dx6P/C6we3s7EhEA7rbb7wQQjTKZPSNOmz64eIGVxJ1VWq1b0Tmp7sAB/vp0ahV5Jc3P6kZNmwYLpeLjIwMoqKuJS/v8waLZ1os/pQpfrjLzi7rPnToEGVlZQwfPpzs7PfR68MICZ7ciqvqQXRm37OM1nRZktQIIXoEu/0Up3I+4lTOB6jVvpGNior9nRxVy5hMvhGTjN2/xut11byu16pJjvBn+/HSZvcVEBBASkoK69evJzLiWhTFzcmT/22wfYkmGMp9SY2iKKxdu5bY2FhCQnTk5HxITMwtqNW61l1YT6E/PadJJgt3WZLUCCG6Pbe7ks1bZrJv3x8pKlqD11uNwRDZ7hW2O1pg4EhSU56mrGw7a75PIydnSU2V7GFxAezPtbWovylTplBWVkZ6eibR0TdwPOtVHM76N48r14aiq8oDYM+ePZw8eZLzzz+fY8f+iVptIC52VtsurifQnq547mjZ5yDOHUlqhBDdXk7OR7jdZYwds5yJE9YzccJGxo5Z1uV2Dm6KSqUmKupnDD/vXbxeJ3v3/Z7v156Hoiho1Wo83rr7yDQmIiKC0aNH891332H1vxWVSsuBA3+pSZR+rNoYjtlRQHl5OV999RUDBw4kKCiXUzkf0KfPA+h01va6zO6r+Kjv+UTDk65F55KkRgjR7WWfep/wsBk19ZwMhnC0Wv9Ojqr1AgNHMmmi7w+n213OyZNvc6q0msgAY4v7uvDCCwkKCuLDD78iMeHPFBR8w/Hjr9Rp5/aLwOgqZfHixajVai66aCi798whOGhCt1tF1mEiTu/R49e8/YLEuSdJjRCi27Pbc/C3DunsMNqVXh/CxIk/AHD4yFOcLC6ktMpV7yhL4/3ouemmm3A6nXz6aRbhYXdy5OjTHD32AopytpaUwxjBN8pU8vPzufrqkew/8At0OiuDBv2zG64i6yD+p+teaZpXrkKce/JfqhCiW3M48vF4KtCcnhzckxj0oUycsJEA63lcEPUa6SdK+Xh7dtMH/kRwcDB33nknarWaTz/14HJeyrFjz7Pph6vZs/c9li//jPVZLjx+Li68sIDjWfeg14Uw/Lz30OtDOuDKuin96dVPsgFflyUFLYUQ3ZaiKGzddi1arT9BQWM7O5wOYTCEk5r6DCWlExkcspf/bgrk2hGxLe4nODiYX/3qV2zcuJEffvgBjfZikpK2UVX1FzRaGDfB166qyo/ExP8jMeHumiXm4jTd6dVPUv+py2rRSM3LL79MWloaVqsVq9XKuHHj+Prrr2ver6io4L777iM2NhaTyURKSgovv/xyo32+9dZbqFSqOg+7vfZ+DC+99BJJSUkYjUZGjBjB2rVrWxK6EKIHsttPYLdnM3Dg/Jr5ND2R0RgFQLEzjr5hzSuVUB+dTsfkyZP53e9+x623/J20Ie/Tt8//SBn4LFGhv+W8XWWEqB+mT9KvJaGpj1YPaq2M1HRhLRqpiY2N5cknn6RfP18J+7fffpuZM2eyY8cOBg0axJw5c1i1ahXvvvsuiYmJLF++nNmzZxMdHc3MmTMb7NdqtXLgwIFarxmNZyfELV68mPvvv5+XXnqJCRMm8MorrzBjxgz27t1LfHx8Sy5BCNGDZJ9a7BulCRzd2aF0OId6GKfK/blwYESb+1Kr1YSHhxMeHg74ksHgoCp0H/+FY0Un29x/j6bzk5GaLqxFIzVXXHEFl156KcnJySQnJ/P4449jsVjYtGkTABs3bmTWrFlMnTqVxMRE7r77boYOHcrWrVsb7VelUhEZGVnr8WPPPfccd911F7/4xS9ISUlhwYIFxMXFNTkKJITo2YqL1xIaelGPn/exavVgdmRrABidFNwh5zCazBxVxaLP7167MJ9zerNsvteFtXqisMfjYdGiRVRWVjJu3DgAJk6cyNKlS8nOzkZRFFatWsXBgweZPn16o31VVFSQkJBAbGwsl19+OTt27Kh5z+l0sm3bNi6++OJax1x88cVs2LCh0X4dDgc2m63WQwjRMyiKl8rKI/j7D+rsUDpMXv5XbNw0Ha+3moMl/UgKMRDm33G3hfIsqZgLMzqs/x5Bilp2aS1OajIyMrBYLBgMBu655x6WLFlCamoqAC+88AKpqanExsai1+u55JJLeOmll5g4cWKD/Q0cOJC33nqLpUuX8v7772M0GpkwYQKHDh0CoLCwEI/HQ0RE7SHXiIgIcnNzG411/vz5BAQE1Dzi4uJaerlCiC6quvo4Xq8dP3OfDunf63Xx7Xd9yci4r0P6b47du39NVdVhANxeDQUVLk4Ud9wogTlhJHGuoxQWF3fYObo9nYzUdGUtTmoGDBhAeno6mzZt4t5772XWrFns3bsX8CU1mzZtYunSpWzbto1nn32W2bNns3Llygb7Gzt2LLfeeitDhw5l0qRJfPDBByQnJ/Piiy/WavfTcveKotR57afmzp1LWVlZzePEiRMtvVwhRBdVUrIJlUpDQMCIDun/TPHH/IKv8XiqO+QcTZk8aVvN19cnf4pOVcozyzuunlXSuKvQ4uXoqnc67Bzdnt4sc2q6sBYv6dbr9TUThUeOHMmWLVt4/vnnWbBgAfPmzWPJkiVcdtllAKSlpZGens4zzzzDRRdd1Kz+1Wo1o0aNqhmpCQ0NRaPR1BmVyc/PrzN681MGgwGDQWbwC9ETlZVtx2IZiFZr6ZD+8/KW1nyt0XTOHjg6XSAXnH+QPXt/B3mfMz1pB0t2B1Jud+FvbP/ikkGxyWw3jSZ039ug/Baa+B/HXklnltVPXVibN99TFAWHw4HL5cLlcqFW1+5So9Hg9XobOLr+/tLT04mK8i1h1Ov1jBgxghUrVtRqt2LFCsaPH9/W8IUQ3ZTTVYRB3/aVQPUpL9/DwUOPdkjfLVVSupm8vM8BGBH2LU63l2W7G7/13hbKqF/Qx32UI9u+7bBzdGt6Wf3UlbVopGbevHnMmDGDuLg4ysvLWbRoEatXr2bZsmVYrVamTJnCgw8+iMlkIiEhgTVr1vDOO+/w3HPP1fRx++23ExMTw/z58wH4+9//ztixY+nfvz82m40XXniB9PR0/v3vf9cc88ADD3DbbbcxcuRIxo0bx6uvvkpWVhb33HNPO/0YhBDdjdEYQ3b2/6isPIyfX7927Tsv74uar/skzWnXvlsqMGAkRkM0AMGcIjUsn68ycrhuZMfMERw29RpOrPsLtrUvw8jmjbD3KjozVMmco66qRUlNXl4et912Gzk5OQQEBJCWlsayZcuYNm0aAIsWLWLu3LnccsstFBcXk5CQwOOPP14r+cjKyqo1mlNaWsrdd99Nbm4uAQEBnHfeeXz//feMHn1234kbbriBoqIiHnnkEXJychg8eDBfffUVCQkJbb1+IUQ3lRD/K7Kz/4etfHe7JzXFJetrvk5MvLdd+24ptVrHhAm+zUb37HmAaPM+DhZ03P5cGo2G431uYvTh5ynNO0FghCywqEUvq5+6MpXS0upo3ZjNZiMgIICysjKsVmtnhyOEaINTpz5g3/65jBu7ErM5qV37XrU6Fa/XAcDUKbs7bU7NT+3bN5eHvwmmxDOSFXOmolZ3zJyX4sI8jC8OYU+fOxk168kOOUe3tWweHPgKfvkdGKygkWpD50Jz/37LpyGE6Ja02gDg7CqlpiiKwsmT7xAYOAp/f982FF6vg8rKw7X2ulEUL4GBoyku9o2OFJdsICz0wnaOvnVc7lJGhh/iP7tSeXLZfuZdmtIh5wkOjWBj0DT6HVuMx/UIGp1Upa5hDoaSY/DU6URa5wdGqy/Bqfc5AIwBtV/TW0BnOv0w+561JlBLjem2kqRGCNEthYVdhNEYR3b2+1itaU22zz71PgcPPQKAv/8gQoKnkHn8JQBSBs4nOvp67PZTrN8wCYD+/f/MsWP/oiD/my6T1CQl/ZZRBZfh0Y/j1e9hREIQ0wdFNn1gKwRP/T/CPv2CjFX/Y8jFd3TIObqlcf8HsSPBXgZ2GzhsP3kug6oiKD5W+z2Ps+m+tcbaiY7mdK0pjQ7UutNfa31fq9SnHyrfM9R9TaUGVD95XdXA6020BVC8gAI/vsFT87UCXg/0PR8GzGivn3aLSVIjhOiWVCoNBkMEbk9Fs9p7f7TXTHn5HsrL95x9T3ED4HDk1bxWWXmYfn1/z/4DfyY+/i4slgHtFHnr+VsGEh19A2OUl9id8A8e/nQ34/qGYO2A5d0Dho1nzxeD0G17HSSpOUtngj5TW36cy342yXFVgqvat4rKVV3P19W+Nh6X7+F1gcd9+tkFXrcvmVC8vkQCt+9rxXv29TPJx49fq3n9J20V5UevN9AWfpT4qKhJdH78deEBKDwoSY0QQrSGw36q2TsKx8TcwomT/8Vur7sJZ3TUtQD4+w/G3zKI8oo9VFUeYUDyXzh46HEKi1Z3iaQGYOCAxzh1ajF3DFnGb5Zdw8K1x5gzLblDzlU+9C7GbnuA7P2biRnY84uGdiid0fewhHd2JB3nzcvAL7RTQ5AbeEKIbis8fAb5BcvxeBxNttVojIwft4rBg/9V85pKpadv3z+gVp+ZM+JFrTGi14cxcOBjqNUGgoPHU1S0poOuoOVUKjVabQAmZSe3jY1j4bpjlFQ249ZGKwybdgu5hJC34oUO6V/0MFVFYO7c4rKS1Aghuq3IyKtxu0spLdvSrPYqlYqI8BkkJd0PgKI40Wr9T3+tsH//w5SXZ5A25OWaZeKhoRdSWroFuz2nQ66hNYYM/hdudzkXxixGURRe+f5oh5zHaDRyIO56UouWUVWa1/QBonerKgKzjNQIIUSr2B2nADAaWjZZtqDgG0ymBIzGGCoq9qMoCgcO/oWc3I8ZOOAJAgLOq2kbEX4ZGo2RUzkftWvsbREcPJ6BAx6hquR/3DhcxcJ1R3lu+QFsdle7n6v/jP9DpcDBr/7ddGPRe3m9p5Oa4E4NQ5IaIUS3lZf3BX5+/Vu8+Z5WY8Hil4zTWYjZlEh+/pdkZ/+Pfv3mEhV1de22Wgvh4ZfWlCroKqKiriUwcAzDTL9jWIyW/6w5yr3vbsPlaX5ZmuaIjo5ji/+FRB96D8XT/kmT6CHspaB45PaTEEK0VknJRkKCJ7f4uNKyLag1JrxeBwZDBIePPEVo6EUkxP+i3vZW/yFUV2ehKJ62htxuVCo1g1KfxayrZnbqA7x2a182HyvmT0syaO89Vc2T7iVcKeTw2g/atV/Rg1Scvj3p3zFbDDSXJDVCiG7L338wtvLdLTpGOb089UwV7mOZ/8Juz6Zf3z80eIzJFI+iuKiqOtb6YDuA0RjFqJFL0GhMmCvn8eTPBvHB1pPc/NoPnChuv6KLw0ZNYZcmFX54pd36FD1M+ek5Z5LUCCFE64SdnsTrcpU1+xiPpxKVSkNc7B1oNH44HHnExf0cP7++DR4TGDgajcZMQcHy9gi7XVmtaQxNe4Xy8gyGWN/lnTtHc7iggrmfZLTbOdRqFUWD7qB/9U6Kj2xrt35FD1KW7Xu2SFIjhBCtEhg4GvBis6U3+5jCwu9QFA9xcbOYPGkbkydtI7n/nxs9RqMxEhI8hdy8z7vULagzAgKG07/fXE6ceINg1xPcO6UPG44UsnhLFg8sTueNdW0fYRox/TZylBBOLX++HSIWPU7xEQiI8+3F04kkqRFCdFtmcx8MhiiKi9c33RhwuWwcPvwPQkMuwGSKR63WoVI1ryhkdMyNVFYerLUT8Y85HHmUlm3j2LEXsTty8XpdeDz2Zl9LW8XF3UFY2HTy8paSpH8Tk07NHz/O4PNdp3jki71sOFLYpv6tfmZ2RV1Lv7yvcdra1pfogYqOQHDzNsLsSJLUCCG6LZVKhZ9fP6qqjzfZ1uksYkf67Xi8VQwY8PcWn8toiALA46k7V6W4eD3r1o9n27brOXpsAevXT2DV6oFs3HjBOZ2HM2Twvxk48Amo+ohnJs3h3xfOY9PvQxmZEMQ/lh1o8wTivtP/D5Xi5cDKN9opYtFjFB2BkJatQuwIktQIIbq1gIDhFBevbzR5sNtPsW37DTgcOQw/7z2MxugWn8dojAVU9SZQxcXrar4OCZlC3z4PEh5+KU5XIT9svoyKykMtPl9rqFQqYqJvYNLEzZw35CmsJhPpO25ievwn7DxRytsbMtvUf7+kBE7p4ik5vqt9AhY9R0kmBCd1dhSS1Aghurf4uJ9jNEazc9c9VFYeqfN+ZeVhdqTfgdfrZMTwD/D3H9Sq82g0BoyGKKqr69aOiov7OWZzEmq1iaKiNbhcxfTt8wCTJ+1Arw/lwP6H8XrP3R4vWq0/kZFXMmH89wAkGZdw6+hg/vb5Xpbtzm1T35XmWPyqstsjTNGTeBy+Yp+dTJIaIUS3ptX6kzbkJbyeKrZuu56KigM171VXZ7Nt+40oipvzhr2N2ZzQpnNptBY8nso6rxsM4Ywbu5Lzp+4mKvIask4sZNMPl5B96n0GDnicMtt2jh079xNs1Wo9CfF3o1LB1IBbmdQ/hAc/2klpVetrRbn84wh1dZ2SEaIL8J6p5N28+WkdSZIaIUS35+fXj9Gjv8RojCZ9511UVh4m+9Ritmy9Co3GwsgRH2E2t31oXKcLwuksarRNaupTjBv7HSEhUzl8eD5Hjz5Hn6Q5ZB7/T4v31GkP/fr9keio61Gp4DdjfsDl8bKwDauh1MFJRCn5VFQ3XURU9BJFh3xJTUjD2yKcK5LUCCF6BJ3OytChrwGw6Yfp7N8/j6DAMYwa+RF6ffvUowmwDqW4eC1ud0Wj7czmBIamvUJ09I3YyncRGnoBOl0wOTmftEscLZWSMp+YmFux5b/AtKSdvLk+k2pn65ammyP6oFd5yMvObN8gRfd1ciuggujzmmza0SSpEUL0GEZDJCNHfEhqylNMGL+OIUP+hV7fflWDY2Nvw+22UVS0ulntgwJHA+BylaLRGFGr9e0WS0sl938YgHHhS6hwuPluf36r+gmMSQag7NTBdotNdHPZ2yA0GYwBnR2JJDVCiJ7FaIwiKuoajMaodu/bYIhCozFjdzRvTsmZW1V6fSh2ezYWv+R2j6m58gu+BiDCXEhSiIZNRxu/jdaQkBjfst3q/KPtFpvo5rK3QcyIzo4CkKRGCCGazeUqweOpIivrjWaVZsg8/h/0+jDc7nIA/Cydl9SEBE+p+bpPsJ2DeeWt6ketN1GgCkYpzmynyES35qqGvN0QK0mNEEJ0Ky5XCQBOZz7btt9Yk6zUp6LiAC5XEdFR11FV5VtqbjLGnpM463P02D9rvva2cRO+Il0UuvKstoYkeoLcDPC6ZaRGCCG6Gz+/vlxw/mHGjvkGhyOXrduua7BEg8NZAEB09A2YzIkAnDjx1jmKtC6dLggARYGdJytIjfZrdV+V5lis1bJXjcB360ljgPDW7f/U3rSdHYAQQnQnZ0ozDB/+Pvv3/4kd6bczZPBLhIdPr9XOYIgAIC//SxITfkVkxEyKitbQp8/9nRA1hIdN59ix5ymoDqXYHsyb608wODqYa0bUP3qkKApf7MrhpdVHqHK6Ad8uJCqViusr/LkO2atG4EtqooaCtvMmwf+YJDVCCNEK/paBjBi+iO07bmbvvgcJChqHTmeted/i15/4uLs4cuQpLJYBmM1JFBV/32nxWiwDSE15CteeeVwYv4bsiij+8LHCxP6hRFhrV1Y+UlDBXz/bw7rDhVwwMJzkCH8UTt+yUiCuIIXQYx+Bswr05k64GtFlZG+D/tObbneOSFIjhBCtpFbrGDL436zfMJHc3E+Ii7uj1vv9+8+jsOg7cnM/IzTkfFyuEioqDmLppAnDUVHXEBg4iuQ+S9h54A3mrHmCH44Vc+VQXy2saqeHF787xGtrjxIVYOLNO0Zx/sDwuh0dL4FjQGkWhA88txchug5FgdITXWLTvTNkTo0QQrSBwRBOaMgF5OV9Ue/7Ol0ILmcRYWHT0Gj8yM399NwG+BMmUzyRkVdiNVSQEFjBV7tyUBSF5Xtyuei5Nby+7hi/mhTNaz/bz9gEe/2dBJ4uN1HadHV00YPZy8DrAr/22wuqrWSkRggh2kitMaJqYGM9j6cKr9eBRmMiMHA0Zbb0cxtcPczmJPr3/zMXZn/KG3tuZfLTqzhRXM2EPnoenrwSo2slJ7MqyMl+mYEDHiUycmbtDvyjQKP3VWYWvVfOTt9zcNcZqZGkRggh2shuz8FojK73vejo6zh48O8UFa3F7SrFq5y7at2NiY25jQv6vElo8AHyPdMZ3zeESO8TFBR8SXDoNBIS7+XkibfZs/cBXO4y4mJvP3uwWu0brSk81HkXIDpf+v8gIB4ih3R2JDXk9pMQQrRRRcU+7PZTeL11q1/HxtyGVuuPzbaTuPi7KC/f3eAy8OYoKdnEzl2/aku4AKjVWmKir2eg+TVuGvgxyX6fUFi4nL59fk9a2n8IsA4lNfVp1GoDFRX763aQMA6Odd7EZ9HJCg/D7o9g7D2g6vzq3GdIUiOEEG3k8VRSVraVzVuupLr6RK33VCoVZnNfcnKXEBZ6ISZjPJnHX0Zp5QZ4uzLupbBwJd+tGtDmqt/x8b8kMuJy8vO/5uixFwkLm0bsj0ZkTpx8B6/XSXTUtXUP7ncRFB7wTRYWvYvHBR/f5RutG3FHZ0dTiyQ1QgjRRmNGf83gwS/i9TrYsvVnlJZtq/V+v75/pLo6k4rKgwQFjaWkZCNbt13XqnPFxd0JgKK42bJlJhm7f9PquDUaA6mpTzNp4kbOn7qbIYNfRKv1bcpXXX2SI0eeJTb2NgIChtc9OGkKqDRw+NtWn190U8fXQ046zPw36Fu/iWNHkKRGCCHayGJJJiL8UkaO+BizuQ/bt9/KqVMf1LxvtQ4GVFSU72XgwCcAsNl2tOpciQm/wmg4O38nP//LNsV+hkqlqfX98axX0Wot9O3zu/oPMAVC3Gg4vLJdzi+6keMbwBQMcWM6O5I6JKkRQoh2otcHM/y8d4iKvIp9++eyZ+/v8Xiq0GjM6HRBVFQcACAwYBTBQRNaeRYV0TE31nyXnPy3tgdej+LitQQGjkKrtTTcqN+FcHSN73aE6D0y10PCeN+E8S6mRRG9/PLLpKWlYbVasVqtjBs3jq+//rrm/YqKCu677z5iY2MxmUykpKTw8ssvN9rna6+9xqRJkwgKCiIoKIiLLrqIzZs312rzt7/9DZVKVesRGRnZktCFEOKcUKsNDBz4BKkpz1BQ8A27MmafLm5ZjNU6FLfbRmnZFkJCz29V//sP/JmjR5+r+T4ifEZ7hV5DURSqq7PweKoab9jvInCWw4nNjbcTPYfLDie3QEJrk/KO1aIl3bGxsTz55JP069cPgLfffpuZM2eyY8cOBg0axJw5c1i1ahXvvvsuiYmJLF++nNmzZxMdHc3MmTPr7XP16tXcdNNNjB8/HqPRyFNPPcXFF1/Mnj17iImJqWk3aNAgVq48O8yp0Wjq604IITqdSqUiKupqDIYI0nfeSfpO3zwYq3UIWq0VrdYfr6eBje1+oqjoewoKv8XlKsbhyKWsbHut98vKdhAWNq1d46+uzgTA35LSeMPIoWAO9d2CSuyaf+REO8veBh5Hl/28WzRSc8UVV3DppZeSnJxMcnIyjz/+OBaLhU2bNgGwceNGZs2axdSpU0lMTOTuu+9m6NChbN26tcE+33vvPWbPns2wYcMYOHAgr732Gl6vl2+/rT35TKvVEhkZWfMICwtrxeUKIcS5Exw8noEDHsXhyAVAUTyoVCp0umBc7jK83qZv2+zbP5fs7HfJz/+KsrLtWH6SaKjVhg6JHSAouIk/XGq17xbU4RUdFoPoYo5vAEMARAzu7Ejq1eobYh6Ph0WLFlFZWcm4ceMAmDhxIkuXLiU7OxtFUVi1ahUHDx5k+vTmF7uqqqrC5XIRHBxc6/VDhw4RHR1NUlISN954I0ePHm2yL4fDgc1mq/UQQohzKSrqWhIT7iUu9g7MZt/OqxqNmRMn3mTN92nYbLsaPLa8fB8ORy5paa8yYvhi0ob8h8DAkQCMG7uSCy84QkjI5HaP2VdhXIW9Orvpxv0ugtwMKM9t9zhEF3R8HcSPBXXXvFvS4qQmIyMDi8WCwWDgnnvuYcmSJaSmpgLwwgsvkJqaSmxsLHq9nksuuYSXXnqJiRMnNrv/hx56iJiYGC666KKa18aMGcM777zDN998w2uvvUZubi7jx4+nqKio0b7mz59PQEBAzSMuLq6llyuEEG2iUqno2/f3JCc/jOr0JmUDBz5OeNgleL1Otmy9Gq/XXe+xBw7+BYC83KVs234DuzLu4eTJ/xIScj4mU2KHxazRmDGZEqio2Nd0474XACo48l2HxSO6CEeFb6Smz9TOjqRBLU5qBgwYQHp6Ops2beLee+9l1qxZ7N27F/AlNZs2bWLp0qVs27aNZ599ltmzZ9eaC9OYp556ivfff59PPvkEo9FY8/qMGTO45pprGDJkCBdddBFffulbwvj222832t/cuXMpKyureZw4caLR9kIIcS4EWIcyePDzxMTcCkBJ6aZ620WEXwZAXv4X+PsPIinpt4we/SXDhr5ekyB1ZIw/nb9TL79QiB4mS7t7gyPfgccJA9p/cnp7aXHtJ71eXzNReOTIkWzZsoXnn3+eBQsWMG/ePJYsWcJll/l+EdPS0khPT+eZZ56pNfJSn2eeeYYnnniClStXkpaW1mhbPz8/hgwZwqFDjdcdMRgMGAwdd79ZCCHaom+f+8nOfpfKykOEBE+koGAFe/b+DrM5EZVKi822E7O5L1VVRygv34PBEEVQ0LhzEpu/dQj5BctQFKXpBKrfRbDldfB6uuxtCdEODnwNYSkQnNTZkTSozYvMFUXB4XDgcrlwuVyof7JuXaPR4PV6G+3j6aef5tFHH2XZsmWMHDmyyXM6HA727dtHVFRUm2IXQojOVFWVCYDNtguns5DDR57BaIzBZIzHbs8mLHQaqSlPEhl5FWFh07HbT7Fjxy0cOjyfwsJVOBwFHRab2ZSI1+vAbm/mvJrqEjjVug0FRTfg9cDBZV16lAZaOFIzb948ZsyYQVxcHOXl5SxatIjVq1ezbNkyrFYrU6ZM4cEHH8RkMpGQkMCaNWt45513eO65s3sq3H777cTExDB//nzAd8vp4Ycf5n//+x+JiYnk5vomm1ksFiwW36ZPv//977niiiuIj48nPz+fxx57DJvNxqxZs9rr5yCEEOec1ZpGTMyt5OR8SF7eUgBSU58lKvKqWu3OlCnwel3s3vMbsrJeJyvrdVQqHRHhlxIXdwdWa+Mj3D+mKAoVFfswm/ug0RjrbePvPwiA8vLdmEyxjXcYM9K3IubwSoht+n9MRTeUvR2qiyH5ks6OpFEtSmry8vK47bbbyMnJISAggLS0NJYtW8a0ab49EhYtWsTcuXO55ZZbKC4uJiEhgccff5x77rmnpo+srKxaozkvvfQSTqeTa6+tXTDtr3/9K3/7298AOHnyJDfddBOFhYWEhYUxduxYNm3aREJCQmuvWwghOp1KpWHggL/Tt8/9HM9aCFAnofkxtVrHkMEvcTL7XfzMfamo2M+Jk++Qm/cZAdbz6Nv3QYKCmt66vsy2nW3brgcgJeUf9RasNBjCMRqiKSndTHh4E3/INFroO9WX1Ex9qMnzi27o+DrQ+UFMPXXAuhCV0tpSsd2QzWYjICCAsrIyrFZrZ4cjhBBtpigeCgu/I/P4fygv38Pw896tWfbdEI/Hzuo1g2q9dsH5h+vMnTlw8BHy8j5n/Ljv0Gr9Gw9k+3/h89/Ag0fAHNx4W9H9vHstKB64bUmnnL65f7+7XuEGIYQQzaZSaQgLm8aI4YsICDiPXRmzsdtPNXqMRmMkOvqGWq8VFtattp2QcDceTxXHs15rOpB+F4LihaOrWhS/6AY8bsja1GVLI/yYJDVCCNED+G5N/QuN2sCujHvwNFGGYUDy3wkMGFXz/eEj/6Cq6litNkZDJLGxt3LixNu4XE1sXmqNhvBBcLhuciS6udxdvhpfic3fc66zSFIjhBA9hF4fQlraK1RWHmH//nk0NrtArdYxZMhLNd9XVR1l67Yb8Hiqa7WLj/sFiuIkO/vdpgPod6FvXk3vmdXQOxxfD1ojRHft+TQgSY0QQvQo/v6ppKb8g9y8z8jNbXz+g15fe+6Ly1VUU6fqDIMhjKjIazhx8m28XkfjJ+93EVTkQd7uVsUuuqjM9RA7CrT6zo6kSZLUCCFEDxMRcTlhYdPJPP5Sk23HjP6q1vf1lV+Iib0Vp7OQkpLNjXcWP9a3QkZ2F+45FAVO/AAJ4zs7kmaRpEYIIXqgiPDLqKo6hsOR12g7l6u05uuAgBH17h5s8RuAwRBFfsGyxk+qNUDSZDgkSU2PUXzUtz9N7OjOjqRZJKkRQogeyGT27eOVn/81paVbKa/Yj6LU3t1dURSOHlsAgEZjwaAPr7cvlUpFbMwt5OR8THV1VuMn7nchnNgEjvI2X4PoAk5u9T138f1pzpCkRggheiB/yyC0WitOVzHbtt/A5s2X8d2q/nz7XV+8XhcANttOSkt9t5Q8ngoGDnyswf7i4u5Apwvi8JGnGz9xvwvB64Zja9vtWkQnOrkFQvp3m72HJKkRQogeSKVSYbUOpaLiQJ33sk7vO2M0RqFS6TAYfHX0XK6SBvvTaEz06/dH8vO/oqTkh4ZPHNwHgpLgiCzt7hFObvFNEu4mJKkRQogeymIZQEXFfgYkP1Lr9SNHn0VRFDQaC4riwuHIAaCsrPGClJERM7FYUjl27IXGT3xmabfo3lzVvpVssSM6O5Jmk6RGCCF6KH/LIOz2k4SETKnz3ner+rHme18RzIjwywGoqs5stD+VSkWfpF9TUrqJ0tKtDTfsdxGUZELRkdaGLrqCnF2+W4kx3adIqSQ1QgjRQ4WGXohabSQn9xPi4+6qt01S4q+JjvYVt1SrDc3ocxoGQyT5Bd803ChxEqh1cOS7VsUtuoicdNDoITy1syNpNklqhBCih9Jq/QgKGsPJk//FVu7bEK+AsFptTma/R3DwBPT6UFzOYgA8nmqKitZi/8lGfOAbrQkJnkxR0fcNn9hg8e1ZI7egurecnb6EphtsuneGJDVCCNGDBVjPw+UqxunM53/cxv2q/7CG82ved7mK8XrdOJ3FGE2xAOTnLyN95x2sXz+B8vK9dfo0GKOoqjqM213R8In7XehbAeVuYhdi0XXl7ISooZ0dRYtIUiOEED1YZOTVhIZeyHnnvcvfxj4IwKuq+wgMPpvYqNVazOY+2Gy7ALBYkmveUxR3rf7c7kpOnnyX8PBL0WotDZ+474XgqvRVdxbdj8sO+fu6XVKj7ewAhBBCdByTKZahaa8CEAf0Nxs4VOXgTf3DXKA+RHLoMACiIq/myNGnyctbWut4qzWt1vcnT76N211O/35zGz9xxGDwC/ct7e5Td6Ky6OLy9oDi6XZJjYzUCCFEL/LJef0A+CivhNnK86QOWgCAv39Kk8d6vQ6OZ71GTMwNGI3RjTdWq08v7ZbJwt1S9jbfZO+IwZ0dSYtIUiOEEL1ImF7HffFnyyFk2Z0A+FkGoFI1PnhfWXUMt9tGeNiM5p2s74WQlwHldScciy7u5BaISgOdsbMjaRFJaoQQope5MMRa8/X6Et9kX6MhkvDw2slKn6T7a31vNESjUukor6g7ebhefc8HVLK0uzvqZjsJnyFJjRBC9DIjrGb0p6txRxp0Na+HhV1cq92ZYpdn6HRWQkImk5/fRLXuM/xCIXoYHJaSCd1KZRGUHOtWm+6dIUmNEEL0Mnq1msXD+rJoaJ9aozYR4ZcyccImzp96gMiImQA4nIW1jg0MGElFxb46Fb8b1PdC30iN19Nu8YsOln16t+hYSWqEEEJ0A+MCLUwNttZ53WAIQ63WEh19IwC5OR/Xet9ojMHjqcTjqWzeifpdCNXFvt1pRfeQtQnMoRCU2NmRtJgkNUIIIeoIDBxFfNxdHDn6LOXl+2peV6t9u8t6va7mdRQ7CgxWWQXVnRxa7qvfdfoWZXciSY0QQog6VCoVffv+HrO5DwcPPXr2dbVvDo5XcTavI40OkiZLyYTuojTLV5k7eXpnR9IqktQIIYSol1qtJyH+bkpLf8DpLPK9pvIlNYq3mUkN+OZm5O3piBBFezv4Dai10PeCzo6kVSSpEUII0aDg4EkAFBevA0CnDwHA+ZMJxI1yVIApsL1DEx1h72eQOLHbfl6S1AghhGiQwRCGv2UQhUWrATAZfUUvCwpbsEy7qgjMwR0QnWhXZdmQuQ6GXNfZkbSaJDVCCCEaFRI6lcLC76iuPlFTxNLtLm9+B1VFvtU0omvb8wlo9JByRWdH0mpS0FIIIUSj4mJnkZv7KRs2TsVo8NV8stl2Nr+DqiKwxnRQdKLdZHwIyReDMaCzI2k1GakRQgjRKL0+hCGD/4VeH4ZaYwCgvHw3Ho+jeR1UFvh2FxZdV8FByNkJQ67v7EjaRJIaIYQQTbJa05g0cRPjxp5dmu1ylzTv4PJc8I/qoMhEu8j40LefUP+Lm27bhUlSI4QQokVMxngAdNpm3KZwVoLDBv6RHRyVaDWvB3YugtQru11V7p+SOTVCCCFaZPDgF8jJ/QSNxtR04/Jc37MkNV2P1wseB+z/EsqyYMSdnR1Rm0lSI4QQokWs1iFYrUOa17gmqZHbT+eEsxIKD/rmyBQegIIDUHocXHZwO8BtP/vw/GgDxcghEDO88+JuJ5LUCCGE6DjlOb5nS0TnxtHTVBb6EpbCA2cTmMJDUHbibBv/aAhLhpiRoPcDrdH30J1+1hp8zxo9RJ/XLWs9/VSLkpqXX36Zl19+mczMTAAGDRrEX/7yF2bMmAFARUUFDz30EJ9++ilFRUUkJibym9/8hnvvvbfRfj/++GMefvhhjhw5Qt++fXn88ce5+uqra7V56aWXePrpp8nJyWHQoEEsWLCASZMmtSR8IYQQ51pFHuj8wODf2ZF0P16vL0kpPPiTBOagr/I5gEoNQUkQNgAGX+N7Dk2G0P7deml2a7UoqYmNjeXJJ5+kX79+ALz99tvMnDmTHTt2MGjQIObMmcOqVat49913SUxMZPny5cyePZvo6GhmzpxZb58bN27khhtu4NFHH+Xqq69myZIlXH/99axbt44xY8YAsHjxYu6//35eeuklJkyYwCuvvMKMGTPYu3cv8fHxbfwRCCGE6DDlOb75ND1gFKDDuB1QfPR04vKjBKbwMLirfW20Jl+iEjYA+l3oS1zCBkBwH9+IiwBApSiK0pYOgoODefrpp7nrrrsYPHgwN9xwAw8//HDN+yNGjODSSy/l0Ucfrff4G264AZvNxtdff13z2iWXXEJQUBDvv/8+AGPGjGH48OG8/PLLNW1SUlK46qqrmD9/frNjtdlsBAQEUFZWhtVqbemlCiGEaKmPfwG2U/Dzrzo7ks6lKFBd4kteahKX088lmaB4fO1MwWdHW2pGXZIhIA7UvXfBcnP/frd6To3H4+HDDz+ksrKScePGATBx4kSWLl3KnXfeSXR0NKtXr+bgwYM8//zzDfazceNG5syZU+u16dOns2DBAgCcTifbtm3joYceqtXm4osvZsOGDY3G6HA4cDjObg5ls9lacolCCCHaqjy39618qiqGvN2+yuS5u6FgPxQdAnvZ2TYBcb5kJXm6bwQmdIAviZFNCtukxUlNRkYG48aNw263Y7FYWLJkCampqQC88MIL/PKXvyQ2NhatVotareb1119n4sSJDfaXm5tLRETtCWQRERHk5vpmzBcWFuLxeBpt05D58+fz97//vaWXKIQQor2U50LU0M6O4tzI+Ai++RNUnP7bpDFAeIrvMWAGhPT1zX8J6QcGS+fG2kO1OKkZMGAA6enplJaW8vHHHzNr1izWrFlDamoqL7zwAps2bWLp0qUkJCTw/fffM3v2bKKiorjooosa7FP1k3utiqLUea05bX5q7ty5PPDAAzXf22w24uLimnupQggh2qo8t3esfCrPhRV/9c2BufYNiBjim++ikUXG51KLf9p6vb5movDIkSPZsmULzz//PAsWLGDevHksWbKEyy67DIC0tDTS09N55plnGkxqIiMj64y45Ofn14zMhIaGotFoGm3TEIPBgMEgE6iEEKJTOCrAWd479qhZ/wLYTsK9GyEitbOj6bXaPOtIURQcDgculwuXy4X6JxOZNBoNXq+3wePHjRvHihUrar22fPlyxo8fD/iSqBEjRtRps2LFipo2QgghuqCKPN9zT59TU54Hm/7t+9pZ0bmx9HItGqmZN28eM2bMIC4ujvLychYtWsTq1atZtmwZVquVKVOm8OCDD2IymUhISGDNmjW88847PPfcczV93H777cTExNSsWvrtb3/L5MmT+cc//sHMmTP57LPPWLlyJevWras55oEHHuC2225j5MiRjBs3jldffZWsrCzuueeedvoxCCGEaHdnNt7r6SM1uxb5noP7gCmoc2Pp5VqU1OTl5XHbbbeRk5NDQEAAaWlpLFu2jGnTpgGwaNEi5s6dyy233EJxcTEJCQk8/vjjtZKPrKysWqM548ePZ9GiRfz5z3/m4Ycfpm/fvixevLhmjxrwLfsuKirikUceIScnh8GDB/PVV1+RkJDQ1usXQgjRUWpKJPTwOTUHlkHyDLh5UWdH0uu1eZ+a7kT2qRFCiHNow79g9XyYl93ZkXSs+XEw6QGYOKfptqJVOnyfGiGEEKJRZ3YTbovio7D7E3BV+3bOVal9D7XGtzQ6Yfy5ueVTUQDb34YTm8HrhiHXQupMX00lxQtqXcfHIJokSY0QQoiOUZEHllYmNW4nbHgBvn/aV3DRGOArJ6B4fQ+vGxw2QAVRaZA46fRjYvvuAZO9HTa/Crs/BpUGkib5qlt/ei989SBMvN83OVjKQHQJktQIIYToGK3dTfj4Rvjifl/V6fH3wZQ/+kZEfqokEzLXwbG1sGcJbPwXJExoe0kGrxf2fAI//AdOboGAeLjgz3DebWAOPn3u475kZ9UTvu8NMqWhK5CkRgghRMcoz2nZbsLVJbDyb7DtLYgZCb9aA5FDGm4flOh7nHerr7bSopvBVdW2mAFWPQ5rn4GkKXDj/yD5Et/trlrnToDpj8PQG6Gy0NdWdDpJaoQQQrQ/RQFbTvOWcyuK7/bOsod8t5gufQZG3lk3kWiMSuVLohpLgprjyHew9lm44GGY/Pum27f1fKJdSVIjhBCi/TnKwVUJ1iaSGlsOLP01HF4BqVfBJU82fUxDSrNg4GWtOxZ8m+h9cjf0mQoTH2iyueh6JKkRQgjR/pqz8d7uj+GLB3yrmm5aDAMuaf35HBVQVQSBrdy/zOuBT37hW1n1s1dB3eYN90UnkKRGCCFE+2ssqakq9q0c2v2Rb3Tm8n+enYDbWmUnfM+B8a07fu1zvgnHt38GlvC2xSI6jSQ1Qggh2p/tTFLzk9VPh1fCZ/f5JvResxAGX9M+y6FLs3zPBn/I2QllJ32P0izfc2UheF3gcfmWg3vdtb+2ZftWWfWRCb/dmSQ1Qggh2l95jm9TPJ3J972zEpb/Gba+AX0vgJn/Bmt0+53Pdsr3/PKPCh1r9BAQ63v4R/q+12h9G+VpdKDWnn22RPgmJ4tuTZIaIYQQ7a/8Ryufsn6AJb/y7Vtz6TMw6hftv1ndwMt8m+JZIiAgzpfI+IXJ3JheRpIaIYQQ7a88B8whsPLvsH4BxIyAWz+GkL4dcz5LOIz5Vcf0LboNSWqEEEK0P1sOZG+FrE2+3XjH/9Z360eIDiT/hQkhhGh/HgeEp8LVr/hqMwlxDkhSI4QQov3N+sJXr0kj1avFuSNJjRBCiPZnCuzsCEQvJNPChRBCCNEjSFIjhBBCiB5BkhohhBBC9AiS1AghhBCiR5CkRgghhBA9giQ1QgghhOgRJKkRQgghRI8gSY0QQgghegRJaoQQQgjRI0hSI4QQQogeQZIaIYQQQvQIktQIIYQQokeQpEYIIYQQPUKvqtKtKAoANputkyMRQgghRHOd+bt95u94Q3pVUlNeXg5AXFxcJ0cihBBCiJYqLy8nICCgwfdVSlNpTw/i9Xo5deoU/v7+qFSqzg6nS7HZbMTFxXHixAmsVmtnh9PryefRtcjn0fXIZ9K1dPTnoSgK5eXlREdHo1Y3PHOmV43UqNVqYmNjOzuMLs1qtco/EF2IfB5di3weXY98Jl1LR34ejY3QnCEThYUQQgjRI0hSI4QQQogeQZIaAYDBYOCvf/0rBoOhs0MRyOfR1cjn0fXIZ9K1dJXPo1dNFBZCCCFEzyUjNUIIIYToESSpEUIIIUSPIEmNEEIIIXoESWqEEEII0SNIUtPLrV69GpVKVe9jy5YtNe2+/fZbxo8fj7+/P1FRUfzxj3/E7XZ3YuQ9U3M/jy1btnDhhRcSGBhIUFAQF198Menp6Z0XeA/VnM/jrbfearBNfn5+J19Bz9Lc3w/wfS5paWkYjUYiIyO57777Oinqnqu5n0d97//nP//pkJhk9VMv53Q6KS4urvXaww8/zMqVKzl69CgqlYpdu3YxatQo/vSnP3HzzTeTnZ3NPffcw2WXXcYzzzzTSZH3TM35PMrLy0lISGDmzJk89NBDuN1u/vrXv7J27VpOnjyJTqfrpOh7nuZ8HtXV1ZSVldVqc8cdd2C321m9evU5jLbna87nAfDcc8/x7LPP8vTTTzNmzBjsdjtHjx7liiuu6Iywe6zmfh4qlYo333yTSy65pKZdQEAAJpOp/YNShPgRp9OphIeHK4888kjNa3PnzlVGjhxZq92SJUsUo9Go2Gy2cx1ir1Lf57FlyxYFULKysmpe27VrlwIohw8f7owwe436Po+fys/PV3Q6nfLOO++cw8h6p/o+j+LiYsVkMikrV67sxMh6p4Z+PwBlyZIl5yQGuf0kalm6dCmFhYXccccdNa85HA6MRmOtdiaTCbvdzrZt285xhL1LfZ/HgAEDCA0NZeHChTidTqqrq1m4cCGDBg0iISGh84LtBer7PH7qnXfewWw2c+211567wHqp+j6PFStW4PV6yc7OJiUlhdjYWK6//npOnDjReYH2Eo39ftx3332EhoYyatQo/vOf/+D1ejskBklqRC0LFy5k+vTpxMXF1bw2ffp0NmzYwPvvv4/H4yE7O5vHHnsMgJycnM4KtVeo7/Pw9/dn9erVvPvuu5hMJiwWC9988w1fffUVWm2vqlF7ztX3efzUG2+8wc0339wxQ+uilvo+j6NHj+L1enniiSdYsGABH330EcXFxUybNg2n09mJ0fZ8Df1+PProo3z44YesXLmSG2+8kd/97nc88cQTHRPEORkPEufcX//6VwVo9LFly5Zax5w4cUJRq9XKRx99VKe/Z599VrFarYpGo1HMZrMyf/58BVAWL158ri6pW2vPz6OqqkoZPXq0cvvttyubN29WNm7cqFxzzTXKoEGDlKqqqnN5Wd1We/9+nLFhwwYFULZu3drRl9CjtOfn8fjjjyuA8s0339S8lp+fr6jVamXZsmXn5Hq6u476/TjjmWeeUaxWa4fELhOFe6jCwkIKCwsbbZOYmFjrttKjjz7Kiy++SHZ2dr2TTRVFIScnh6CgIDIzM0lNTWXz5s2MGjWq3ePvadrz81i4cCHz5s0jJycHtdo32Op0OgkKCmLhwoXceOONHXMRPUhH/H4A3HXXXWzfvp0dO3a0a7w9XXt+Hm+++SZ33nknJ06cIDY2tub1iIgIHnvsMX75y1+2/wX0MB31+3HG+vXrmThxIrm5uURERLRLzGfIWHUPFRoaSmhoaLPbK4rCm2++ye23397gf5AqlYro6GgA3n//feLi4hg+fHi7xNvTtefnUVVVhVqtrllZANR831H3qXuajvj9qKio4IMPPmD+/PntFWav0Z6fx4QJEwA4cOBATVJTXFxMYWGhzDlrpo74/fixHTt2YDQaCQwMbEOUDQcjhLJy5UoFUPbu3Vvv+0899ZSya9cuZffu3cojjzyi6HS6czabvTdq7PPYt2+fYjAYlHvvvVfZu3evsnv3buXWW29VAgIClFOnTnVCtD1fU78fiqIor7/+umI0GpXi4uJzGFnv1NTnMXPmTGXQoEHK+vXrlYyMDOXyyy9XUlNTFafTeY4j7R0a+zyWLl2qvPrqq0pGRoZy+PBh5bXXXlOsVqvym9/8pkNikaRGKIqiKDfddJMyfvz4Bt8///zzlYCAAMVoNCpjxoxRvvrqq3MYXe/T1OexfPlyZcKECUpAQIASFBSkXHDBBcrGjRvPYYS9S1Ofh6Ioyrhx45Sbb775HEXUuzX1eZSVlSl33nmnEhgYqAQHBytXX311rS0QRPtq7PP4+uuvlWHDhikWi0Uxm83K4MGDlQULFigul6tDYpE5NUIIIYToEWRJtxBCCCF6BElqhBBCCNEjSFIjhBBCiB5BkhohhBBC9AiS1AghhBCiR5CkRgghhBA9giQ1QgghhOgRJKkRQgghRI8gSY0QQgghegRJaoQQQgjRI0hSI4QQQogeQZIaIYQQQvQI/w+8Oj8Ir+CKQgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 817\n",
      "working on avg unit-to-HDcp dist for HD 1634\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([1646, 1648, 1649] )  #lock in the clusters  #update this for each state\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce01cce3-e408-4cb1-8761-0cc0be4a57c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 24 HDs\n",
      "working on HD 14 cantFill 0 of 24 .Time is now 0\n",
      "working on HD 1819 cantFill 20 of 24 .Time is now 0\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.94477 0.09401\n",
      "CCB cluster 0 = unit 1646 with pop 251617.0 now has use of 0.8996701430933163\n",
      "CCB cluster 1 = unit 1647 with pop 103725.0 now has use of 1.0676355592737798\n",
      "CCB cluster 2 = unit 1648 with pop 373177.0 now has use of 0.9912867009516644\n",
      "CCB cluster 3 = unit 1649 with pop 284018.0 now has use of 0.999720262693957\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "out of 1994 total HDs, there were 1841 116 0 37 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "\n",
    "#40 5 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 817\n",
      "working on current HD diam for HD number 1634\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38608 but less than 193038\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 153 HDs to triage in this block\n",
      "working on swell-filling discontig HD 0 our 1 th HD we think we can swell out of 153\n",
      "working on swell-filling discontig HD 958 our 41 th HD we think we can swell out of 153\n",
      "working on swell-filling discontig HD 1683 our 81 th HD we think we can swell out of 153\n",
      "working on swell-filling discontig HD 753 our 121 th HD we think we can swell out of 153\n",
      "we partially regrew 82 HDs, leaving 71 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList) )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.75 * aDP or contigPop > 2.*aDP - minPostFixPop:  \n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 0.94477 0.09401\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.94327 0.08556\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1026 time is now 3\n",
      "out of 1994 total HDs, there were 1923.0 contiguous and 1975.0 complement-contiguous HDs\n",
      "19 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 52\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 71 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 733545 district pop vs 772153 target\n",
      "we'll also trim any HD with total islands' pop <= 15443\n",
      "working on HD 11\n",
      "Out of 71 discontig HDs 71 had discontig HDs.\n",
      "Of these, 1 won't be under 733545 after all minor discontigys were shed, while 70 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 70 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116 37 70\n",
      "{0, 1, 4, 1540, 1054, 30, 1056, 1571, 36, 37, 1060, 1575, 1578, 1068, 1070, 1073, 1077, 1591, 1080, 1081, 1082, 1084, 1596, 1087, 1102, 1615, 1104, 1112, 106, 107, 108, 1654, 644, 138, 1675, 1677, 1678, 1682, 667, 1695, 1720, 1723, 1725, 1727, 1728, 1730, 1732, 1739, 1740, 1747, 1748, 1760, 738, 739, 1768, 751, 752, 753, 1791, 255, 264, 1804, 1807, 1810, 1813, 1302, 1304, 289, 831, 832, 1856, 844, 871, 1902, 1903, 1909, 1919, 897, 432, 1460, 1464, 958, 475}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e16dfee-b5bc-4863-8bed-9a6b22290aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes about 3sec per HD :-( The total number of HDs to fix is 70\n",
      "swell-generating HD 11 our 1 th HD we must regrow out of 70 0 sec elapsed\n",
      "swell-generating HD 1059 our 21 th HD we must regrow out of 70 210 sec elapsed\n",
      "swell-generating HD 1644 our 41 th HD we must regrow out of 70 284 sec elapsed\n",
      "swell-generating HD 1773 our 61 th HD we must regrow out of 70 303 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes about 3sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%20 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 0.94327 0.08556\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.94503 0.08605\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 504 time is now 1 sec\n",
      "working on HD 1026 time is now 3 sec\n",
      "working on HD 1534 time is now 4 sec\n",
      "out of 1994 total HDs, there were 1994 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 5 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "0.78053     1646     251617 0.25\n",
      "0.89082     1647     103725 0.25\n",
      "0.88314     1648     373177 0.25\n",
      "0.99972     1649     284018 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.94503 0.08605\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "17caead2-8aba-4fc5-817e-53d440c5a704",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c5d76a90-4441-4e96-b9a7-7de433686812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pre-squaring HD vtd lists to file\n"
     ]
    }
   ],
   "source": [
    "print(\"OPTIONAL - saving pre-squaring HD vtd lists to file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigNotSquaredVTD.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 817\n",
      "working on avg unit-to-HDcp dist for HD 1634\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f179943a-ef19-4036-a8c6-5ab7b1f97bf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Restart\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "512b2e80-04a3-4bd5-9537-ba6a2b9d81cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1648, 1649, 1646]\n"
     ]
    }
   ],
   "source": [
    "print(list(barredJettisonSet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "03bc208c-b1d2-4a51-aeeb-f238c3949c29",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "barredJettisonSet = {1646, 1647, 1648}  #for MD, these are under.  1649 is close to unity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d832b109-2587-43d1-9b05-197ebf73db59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1648, 1646, 1647]\n"
     ]
    }
   ],
   "source": [
    "print(list(barredJettisonSet))  #order is random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "9854465d-3163-4fa2-a151-7862a1396df3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "how much HD weight could pick up our CCB as a neighbor?\n",
      "we will boost unitUse of 1647 from 0.89082 to 1.01815 by attaching to all neighboring HDs with pop after adding of 1.04 aDP\n"
     ]
    }
   ],
   "source": [
    "print(\"how much HD weight could pick up our CCB as a neighbor?\")  #run this for each of three\n",
    "boostWeight, CCBu = 0., list(barredJettisonSet)[2]  #0 #1 #2\n",
    "CCBnbrSet = set(unitNbrs[CCBu]) #set(get2nbrs([CCBu], unitNbrs))\n",
    "receivingList = list()\n",
    "popRat = 1.04\n",
    "for t in popHDlist:\n",
    "    if HDvPop[t] < popRat*aDP - unitPop[CCBu] and CCBu not in HDunitList[t]:  #1.336\n",
    "        if len(CCBnbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            boostWeight += HDweight[t] * nDistricts\n",
    "            receivingList.append(t)\n",
    "print(\"we will boost unitUse of\",CCBu,\"from\",r5(unitUse[CCBu]),\"to\",r5(unitUse[CCBu]+boostWeight),\"by attaching to all neighboring HDs\",\n",
    "     \"with pop after adding of\",popRat,\"aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2f89cffc-2050-429c-93da-192fac45bd7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After CCBu tack-on, its use is now 0.98587\n"
     ]
    }
   ],
   "source": [
    "for t in receivingList: \n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t]+[CCBu],unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        HDunitList[t].append(CCBu)\n",
    "        HDvPop[t] += unitPop[CCBu]\n",
    "        unitUse[CCBu] += HDweight[t] * nDistricts\n",
    "print(\"After CCBu tack-on, its use is now\",r5(unitUse[CCBu]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7f72a570-aa0c-480e-b049-ccebbe24e51f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 504 time is now 1 sec\n",
      "working on HD 1026 time is now 3 sec\n",
      "working on HD 1534 time is now 4 sec\n",
      "out of 1994 total HDs, there were 1994 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 5 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.94503 0.08605\n"
     ]
    }
   ],
   "source": [
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fa933848-9596-4201-8e7b-5dfbf4453ea8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 1519 HDs with pop < 764431 to within 2597\n",
      "working on squaring up HD 3 time is now 0\n",
      "working on squaring up HD 129 time is now 14\n",
      "working on squaring up HD 245 time is now 23\n",
      "working on squaring up HD 356 time is now 35\n",
      "working on squaring up HD 487 time is now 51\n",
      "working on squaring up HD 588 time is now 61\n",
      "working on squaring up HD 723 time is now 89\n",
      "working on squaring up HD 876 time is now 107\n",
      "working on squaring up HD 993 time is now 119\n",
      "working on squaring up HD 1115 time is now 126\n",
      "working on squaring up HD 1215 time is now 142\n",
      "working on squaring up HD 1330 time is now 158\n",
      "working on squaring up HD 1516 time is now 167\n",
      "working on squaring up HD 1653 time is now 174\n",
      "working on squaring up HD 1845 time is now 188\n",
      "working on squaring up HD 2013 time is now 203\n",
      "We have addressed underpop in a total of 1519 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9k31DEpZsOc40jscxFZ1FCFVeuITXi464x9tItBFhRWnQOC9+isot+PzAGdQ0XBOlvAtVTZyWr6uBs0CuMYIgCJ2jZWCwHh5EUm4tOYTcLb6o+D24e0fER4dLjomPDsdgAfHICz+xszIAMMcZsXr3SVVWHxaUBI0XllVh6MubMWlpCZbtqHQSQcA1y1xtQ5PsdbkWtNRjg1cSQgRBEDqH5UHKWkFZLw8isTia+ChpscHjS4sB0Ca2Fk3sIzlm0cQ+giKMJZ5p0q1dNbH8uKJkbQDi8WOO8GJt/vpDmJunLE5Ly7WsFSSECIIgdI6WgcF6ehDlZqZg++wcrC4YjDfu64/VBYPx1pSBTO/1pcWAJzczBe/ePxDmOOcijeY4I96VyXSS6+WVlhTj8fl5ujaar9jw7OelTFYp3jKXEBOhqEeZr4PcWaD0eRkofZ4gCL2gVco7/6sfEO407s8mm75Oi1eDJ/WXxN7LWlLAFf6oD9+Rji8PVqleG4VlVXj28zLUMPRvc+SN+/pjQv8uiufEF+UbWJ/fJIRkICFEEISe0KoIop4K2rmiJ6Hmq6KTLAIwPjocxrAQp8KOjvdM7bmK1fVhYXXBYM2z0bSChJBGkBAiCKK94vogyuqWgL0nar32YFLy4NODUPPFOTjOCZ81BogLQJYMOSXzLFcUUQw9WObkYH1+U/o8QRBEkOKY3l1YVoVhr2zx2kNfqajwVVq8VMsKNa0jlCA0J3xmWt2la5laZherj9J9Ss0za/NaIXwdy+MtyCIkA1mECIJo73i75YGvWiooRUw0zM3LwPz15YpbRyg9tticcAB+N/A6RBtD0S0xGvnZaYgIC5EVOWrmed2BM5i55oCic9eLC1UOco1pBAkhgiDaM2r7Rell/2qREyIsqI2PUeqOYukn9uZ9/fHXr8pFg53F5pk1SDsuMgy/y7oOIzPMfusSrxRyjREEQRCyeLvStK8rWbPEx7D0u2LBYr2M4uPVil13St1RfD8xIfjz/dOaA5LnLjbPLFW+O8ZEoHjObxAR1j4r7pAQIgiCCGK8XWnal5WsWeNjPImLcWT++kNOFhhWl5E3ikGyCjjXY/N1ff64Yp+bNYyXdAt+m9luRRBABRUJgiCCGm9XmvZVJWuxish8cDPftR7QToi4uqGEjiWEP4pB8lReaHB7Ta7Yo95jgTyFLEIEQRBBjJxrhI8tUVtp2tv7B+RdXa6NWr0lRISOJYTaprNasLjoKHqZY93Eja+y9PQIWYQIgiCCGG+3PPDG/lttHIqPV2PdgTMoPl6NkuPVzHFIgHybEU8QagrriidNZz2FF2pCafi+aF6rR0gIEQRBBDm5mSl4a/IAJMQ4NzzVyjWipevFsTP6zDUHMGlpCaav2sf0Xt4l5gshcv5io5tgcxQfYnMih6fnyyLUgg1yjREEQQQ5hWVVVwN/rxXxS4yJwNw87WrFaOF6EUt5r7vcIjjeFUeXGC9EXIOrtaLyQoNbirxrMHVuZgpybuqMj4srcaLmEi41XcG/950RDVoW6icWYgBkaiwK4o2A7UCF6gjJQHWECIJoz+i12KEraltBANK1ilptHD7cUYH56w9pdKZAQnQ4ai+5izPXOWWtLC3VT6y2odluEVPyMPekR5hW6KXXGFmECF3hqwaHRHBD66wNpUHG/kRtyrtcHFJoiAFJsUYPz84ZMUHiOKc2G4fpq/a7jbVeFUBPjrgRaUnRbuvTsS0Kzzsh7JYtLYLTtUAPveR4SAgRukFPHwyi/ULr7Bq+LnboCayunPiocCdXmZnh3mqVRRZiAB7P6YnXvzsqOoaf0+fWlUkK0DV7TjJX23Z1O1ZeaMDioqOiLjZ/9wjzRR83JZAQInSB3j4YRPuE1pkzvix26CmsYuWtKQMRYjAosvaxpLMbDMBDQ9Pwj22VAIStPksmDUALY8COYzyWK2oEqKulqJc51k3wm02RmJvXG6aoCKw7cEaT7vVK0aMVkoQQ4Xf0+MEg2h+0ztzxVbFDFuQevqz1iAbfoDztm88ie3SFePYZxwFZ3RKR1S1R0qJYfLxa0bGl8ESACgWn1zY048Wvf4Clvsk+zhxnxLzxN9t/AHjbYqpHK6Si9Pm0tDQYDAa3f9OnTwcAwW0GgwGvvPKK2744jsPo0aNhMBjwxRdfOG2rra1Ffn4+TCYTTCYT8vPzUVdX5zTm5MmTGDduHGJiYpCUlITHH38czc3OVT5LS0sxbNgwREVFoUuXLnjxxRdBseH6Q8kHgyDUQuvMHbl6Oga0PQS9HU8ilBI/9OXNThWavV3vaGSG2R6oLISjUN4+OwerCwbjjfv6Y3XBYGyfnWMXCSxz2jEmgumcPBWgjnWBrJeb8diqfU4iCAAs9U149Go1bCXVudWiRyukIiG0Z88eVFVV2f9t2rQJAHDPPfcAgNO2qqoqfPDBBzAYDLj77rvd9vX666/DYBBeKpMnT8aBAwdQWFiIwsJCHDhwAPn5+fbtra2tyMvLQ0NDA7Zv3441a9bgs88+w1NPPWUfU19fj5EjRyI1NRV79uzBm2++iVdffRV///vflVwy4QP0+MEg2h+0ztzxtrhgQcnD15utIHZX1DhlarniKJSlCg+yzOn8CZk+FaCtNg7PrC2VHPPM2lLM+/IHyUa0YoUYlaAnKySPItdYp06dnP5etGgRunfvjmHDhgEAzGaz0/Z169Zh+PDhuOGGG5xeP3jwIP7+979jz549SElxXriHDh1CYWEhSkpKMGjQIADA0qVLkZ2djcOHD6NXr17YuHEjysvLcerUKaSmpgIAXnvtNUydOhULFixAXFwcVq5cicbGRnz44YcwGo3IzMzEkSNH8Pe//x2zZs0SFWGE79HjB4Nof9A6E0asng5LkLGnqHFXeqsVhJZCmWVOQ0Ig2ehUSoAqjeEpOV4tKfIAyG7XymXli5YrSlEdI9Tc3IwVK1aIiopz585h/fr1+Oijj5xev3TpEiZNmoQlS5a4CScAKC4uhslksosgABg8eDBMJhN27tyJXr16obi4GJmZmXYRBAB33nknmpqasHfvXgwfPhzFxcUYNmwYjEaj05g5c+agsrIS6enpgtfV1NSEpqZrpsP6+nr2SSFUoccPBtH+oHUmjr/6TKmNFxFKIfcUoWakQvBCWU6MyM2pWgEqF8MjdF7FP11QPB9ieGoxZel27+usNtVC6IsvvkBdXR2mTp0quP2jjz5CbGwsJk6c6PT6k08+idtuuw0TJkwQfJ/FYkFycrLb68nJybBYLPYxnTt3dtqekJCAiIgIpzFpaWlOY/j3WCwWUSG0cOFCvPDCC4LbCO+gxw8G0f6gdSaNN8SFHHpxVxaWVWFxkXjKO+AslFkDiuXmVKkAlct6FKo8nWKKRP/r4yWvTQlaWEz9aYUUQrUQWrZsGUaPHu1klXHkgw8+wJQpUxAZeW3SvvzyS2zevBn79++X3LeQhYnjOKfX1YzhA6Wl3GJz5szBrFmz7H/X19fj+uuvlzxfwnP09sEg2ie0zvSFHtyVvHuOhefHZWBTuUXTEgysArTVxsnG8Ly3tcJtm8XaiG+sFqZziQkPQUOLTXCb1hZTPXW7VyWETpw4gaKiIqxdu1Zw+7Zt23D48GF88sknTq9v3rwZx48fR3x8vNPrd999N26//Xb85z//gdlsxrlz59z2+fPPP9stOmazGbt27XLaXltbi5aWFqcxvHWI5/z58wDgZk1yxGg0OrnTCN+hpw8G0X6hdaYf9OCuZK1Y/cSIGzEyw4yhL29WFNPEEs/DMmbJ5mNuGV8s8OdqMLSVAJBCSgQB2ltM/WGFFEKVEFq+fDmSk5ORl5cnuH3ZsmXIyspCv379nF5/5pln8NBDDzm91qdPHyxevBjjxo0DAGRnZ8NqtWL37t249dZbAQC7du2C1WrFbbfdZh+zYMECVFVV2YOtN27cCKPRiKysLPuYZ599Fs3NzYiIiLCPSU1NdXOZEfpBLx8Mon0TDOssENqI+MJdKTcPrG63tKRoxTFNLC401jGLi44ouWz3c/Mg2au9W0wVCyGbzYbly5fjgQceQFiY+9vr6+vx6aef4rXXXnPbZjabBQOku3btao/Z6d27N3Jzc1FQUID33nsPAPDwww9j7Nix6NWrFwBg1KhRyMjIQH5+Pl555RXU1NTg6aefRkFBgb2x2uTJk/HCCy9g6tSpePbZZ3H06FG89NJL+Otf/0oZYwRBtGsCqY2IN92VLPOgxD2nJKaJpYo5ANkxIzPMzK47OR4ckoYNpRZY6tnblUwf3h1JsZEwRUWg+YoNe0/U6lpcq0GxECoqKsLJkyfx4IMPCm5fs2YNOI7DpEmTVJ/UypUr8fjjj2PUqFEAgPHjx2PJkiX27aGhoVi/fj0ee+wxDBkyBFFRUZg8eTJeffVV+xiTyYRNmzZh+vTpuOWWW5CQkIBZs2Y5xf8QBEG0NwKxjYg33JWs88DSXiMxJhxZ3RKw90Qt07GTOhjx9KcHJV1o8778AYBB1s0WawxX1WxWiJEZZvwlLwO7K2qw49gFLNlyTHJ83eUWLNjwo/3vEAPgWEZIr+JaKQaOSi1LUl9fD5PJBKvVarc2EQRB6JFWG4ehL28WfXDyMTeszTwDFaXzwIsmQLxzfMrVPl3z1x+SjWl69Xf9MGXZLoERypkxvDuWbDnu8X5SXO775/vP4MlPDni0T34F6VFcA+zPb0WVpQmCIAj9Qm1E2mCdh8WbDqP4eDVGZpgFK1Y7YrE2Yvqq/Rjfr+2BL1WJ+0KD8qBmMY6e/0WT/bjGWtX84vk5allx2p+QECIIgmgn+LMuT6uNQ/Hxaqw7cAbFx6v9+mBkvb4lW47b+5oBwH//PByJIn3A+Kv58mAV3pos3eZDy3T/b39wz6JWyrQhaW4WG7HrVEp7ENfUfZ4gCKKd4K+6PHoLzlZ6fXzc0BMjbkRNQ7PoOP6hnxATge2zc0RjmljKAnSOMwIwMAcue0JOb/eSMWZTlKbHOH+xMSAyFYUgixBBEEQ7wR/d5H3RsVwpcvPgCi9Wlu90L0goxPmLjaobr/LHuzk1Dhcbpft7acVT/zrgdh/4OdKKygsNGPryZkxaWoKZaw7YLW3+uP9KISFEEATRTvB1N3m5pqmAf+JH5ISIEBzkG4/ysFic+LIAri40fuq/+/FnNDS3Mp6dZ5yrb3ITpfwceboSDAASosOxuOiorsSwEkgIEQRBtCPEHsCOMSxaoefgbLF5kCM+Klwzi1puZgq2z87B6oLBeHBIGgDn9HNfISZK+TlytQzFR4cDkBeR/HaxS5ITw3qJK6MYIYIgiHaGr9qI6KVpqhiO8/DP4kp8Uybfc+sPQ9LwetFRzSpdh4YYcGt6Imb964CSU2fGtbaPGK5Vr3nE1sqmcotb3JfrscymSNz3q66SVa/FjqunuDISQgRBEO0QX7QR8XfTVJbgXF6IsNTMMccZMSOnJ3qZYzWtdM3az0wNSo0oQqJUaK0ICSS+qKTjfH/9/VnFx9Vb0U8SQgRBEIQq/Nk0VYlFYXdFDVN21qRbuyI0xKC5Rc0bFjGDAZh6WxqW76hU9D4lolRIILn+rVQMy8WVCTWu9TYUI0QQBEGoQuvgbNaYEaWZaqxCpGvHGPv/S2WFKUWNRcwYJn28tyYNwKgM996dUsRHhWsuSpVmKuoxrowsQgRBEIRqtGqaymrhUWNRYBUi87/+AVHhIZq7ZVj6mbnSdEV4ZHxUGBbd3Re5mSlotXFIjImQrH3kyNTbumluZeHF8B9X7GOKq9JjXBlZhAiCIAiPcMyOeuO+/lhdMBjbZ+coEkGsFp6Sn6oVWxRY6wrVNLQoSvdmtWCpSecXgwMQGxmOVhuH0BAD/jYhk/m9v0rzTsyYkkxFf8eVCUEWIYIgCMJj1AZnK7HwbCq34JnPSpn262hRkLJaCMESo6I06yk3MwUP35GOpdsqINbqPDE6HDUytYysl69gyj922Y81pm8KfrOvE7778WeZqwKKf6rGhYYmr2QRssZV+TOuTAyyCBEEQRB+gzVmZMnmo/jjin2ou6yu6CFvtUiQ6bHFEqOippp2YVkV3ttaIZrl9eSInpg7NkPy3MSO9dDt3Znes2TLMdGqz1rU9GGJq/J10U8WyCJEEARB+A3WWJDlOyqZ4mukLAq5mSm43NyKJ/91UPV5sVqwYo3hdutLVrcEPLNW3JJlALBmzym8+rt+sucldKz//nm44hgkx1R1XN2Po7BLjInA3yZkYkxf7dPYtYor0woSQgRBEITfYI0FYbUEAdIWBdZmo2LnxWrBmrJsl/21DsYw/NJ0RfY95VX1iI8KZ75W/n17T9TaXX+s8ELqmbWlsF5qcRNQNQ3NeGzVPjxyOh1zxlyzVGnVWNVXRT9ZICFEEAQRJOixOzhLzIgpOpypD1h8dDgWTewjaVFQGqPiOmcW62Wm63JESgQ5smDDIcX7BtqsVxP6d8HDd6Tjva1sjWMBtv5q722tQL/r4jGmb6rm1aB9UfSTBRJCBEEQQYCeWho4wpJ+/Yfb0iXbOPC8NWkghvRM8vh4vEVJaM4SZWKM/EFSByN2HLuAT/ac9sr+n1tXBsCA6av0Uw1aSwwcJxa/TgBAfX09TCYTrFYr4uLi/H06BEEQihFracA/+PXwEJMSaiMzzBj68mZZK8722TnMFi45YSg1Z758aBoMEM0yM6DNCmYMC4Glvsmr5yFVr0jN/PsC1uc3CSEZSAgRBBHItNo4DH15s2hci54eYlKuO16YAMJWHDViTux4cnPmD4SsV3p7eK8uGKwLVxcP6/ObXGMEQXiMHmNPiDaUtDTw90NMKmbEG5lGYsdjbZLqKzHy4JA0bCi1OPVLM5sicbmllSl2Sgq5QG4l+LIatJaQECIIwiP0GntCtKHHlgZq8VWmEetc+MoiY4qKcDvaxcZm/NJkU73PDsZQvHpPP+w9UYul26QDrBNjwlHTIC+4fFkNWkuooCJBEKpRU1iO8C16bGngCVo2QxVDL3PBxwC9XnTELQbIExHU9v5WHKq6iK8Oyn9GXxyXqaixaqBBQogIerSoqBqMyBWWA9qKtNF8+hel3cGDAbnPPGtvMm/DufxXa9747qiTu02MjrFG3VWD1hJyjRFBDbl11BNIsSfBjNLu4O0duew03u1236+64vWiI5rEAfW/3oQDp6ySY2IiQtHQ3Or8mjHU4xggLeDrFOmpGrSWkBAighax9Nj2UBfDF7Sn2JP2jt5aGvgLqc/8oyv2Id6lcGN8dDgA+aKDchw7/4vk9hhjKBqaWt1eF3rNH/CuQj1Vg9YSEkJEUKKk43Wgf8i9RXuLPWnvKHmIKckCDJSMQRZXrqvg4VtPPDmiJ9KSYnD03EUs2XJc8bF/kRE0ehE8rgj1bdNLNWgtISFEBCXk1vEcpa0KCP/D8hBT4i72p2tZqQBjTYl3hP9RtGbPKWyfnYPdFTWqhFAgEwxuUwqWJoIScut4Dh97ArTPAMpgREkWoD8zBgvLqjD05c2YtLQEM9ccwKSlJRj68mbJY6r9LDv+KOLFfzCQGBMeNOEBJISIoITcOtrAx56YXR4OZlNk0HyJtheUZAH6M2NQjQBrtXG4cNGzFhTflFVhd0UN5ub19ns2mS+YO/bmoPn8kmuMCErIrSMPq+uhvQZQBhtK3MW4+v8sY7V0LauJ7RNy36nhn8Un8M/iE0gxReLhO9Lx5cEqj/fJf0JM0eH2eCS9YI4Lnh+BJISIoIRSiqVRGvvRHgMogw1vuIu1di0rje0TyxLzBIu1Ee9vrcBbkwfiyLmLeP27o6r3lRgTgQW/zQQAPHq1j5q/CcYfgeQaI4IWcusIQ9WigxMl7mJ/uZaViDUp65ErfJo8y88e7uq/59aV4cOd0q0p5Hgur7fdohoTEerRvrQk2H4EkkWICGrIreMMlRUIXpS6i/3hWlYiwFizxObm9cbUIenYVG5R5EKraWhmGieF2RQFACj5qdqtmKI/MMcZMW+892KD9FpqgYQQEfSQW+caVFYgeFHqLvaHa1mJWPv6+7NM+0yKNSI0xOD0o+ibsir8s/iEpufuimNbk+Lj1V49FgtPjrgRf/x1d+w9UYt1B85oLlT0XMWfXGMEQdihsgLBjRJ3sT9cy0pKNqhx3/E/ikZ7+cFsgKtQ9DyKafqvuyMxJlxyTHREqFsQdIopEu/ePxC9zB0w7JUtikoSsKJ3dztZhAiCsENlBQgl7mJ/uJZZ24XIWY+ANleQkPuO5b1qiY8Kx6K7+zgJxewbklQXauStYLNG9cIVmw3vbRWPW7rU3IpXf9cPCTERTvdrU7nFa+2GAsHdThYhgiDsUKdy3yLXBd1f56RE2PBWlAn9uyC7e0efPMxyM1Pw3z8Px9y83vh/2d0wN683/vvn4U4PaynrEU/jFRs2lVvcXnd8r9YYDO73eHD3jvaAbUX7uvrf58dlYFO5RVIE8ePnriuDxXrZfm8BeLUmlNKyDP7AwHGc/z95Oqa+vh4mkwlWqxVxcXH+Ph2C8Dq8GRsQjv0I5ow6LdFjzIQez0kIpW1AnllbKtg4VW5NF5ZV4dnPyzQJjHY9rusxC8uqFKfQ89c8MsOMoS9vVlzXKMUUift+dT0WF8mXAFhdMFhVXOC6A2cwc80B2XFv3NcfE/p3Ubx/KVif32QRIgjCCSor4H30GDOhx3MSQul5jswwIzJMODVdzuKRm5mCF71kGXI9Zm5mCt69fyDMcUbmfTw7pjdMURFYvOmwquKOFmsjkwgC1McFBoK7nWKECIJwg8oKeA89xkzo8ZyEUHOeuytqYKlXlwlZWFaFPzFYM5QidkzHz53Fehnz1x+StEbNXLMfnnhTlbxVrVAJhCr+ZBEiCEIQf8R+BAN6jJnQ4zkJoeY81WZCKinIqJYdx352s0TxnzuzKUrWJeeLkDJP4wIDoTkzCSGCIAgfoscSBXo8J0+O7zhOrWuGtSCjJyzZctwtRZ0PoP/GD65IbwkVvbvbyTVGEAThQ/QYM+HJOfmyWrCa81TrmlEi+vh9vPq7frjQ0ISkDkbM+mQ/zl2UD7Kusjbi0RX78Osbk5DUIRLbjv6McxebmI+tFU+OuBFr9pyULEngCXp2t5MQIgiC8CF6jJlQe06s2VtaiSU156m2wXJSB/agZX4fQ3om2f+ePKgbcyAyAPznyAVFx2MlPiocLTYbGpqEW3jwczYjpwdm5PRAyU/VVytdc8i+IQmDr8YwSd1D1vur1yr+JIQIgiB8iNoHs97OSayzu2sRPi1T8tXOHWsRRicY428SYyLw0m8z3faRlhTDtgMvMf3X3TG0ZyengomA9Jy53qslW44jxRSJ8f1S8OXBKsF7CCAgSi5IQXWEZKA6QgRBeAM91uxRYuGRqlvDWxnm5mVg+ip3seRpTSq1c6fEMsVa/2bxvf3w24HXub1efLwak5aWyF+Ml1j50CAM6XHNQiU0Z/FR4fjDkDTMyOkpWl1aDFch6roN8H/NMdbnNwkhGUgIEQThLfTYjZvlnFgf8okxEaKZT7xY2j47R9U1e3vuWK9RrNBgq41D1vxNqLvsXsjRFwgVKGy1cViy+RiW76hwOi9zXCQar7QKFp1Ui6f3VwtYn9/kGiMIgvATeoyZYDkn1kBiqfRvqfo9LHhj7hzFVVIHI8xxRpyrb1IVyxUaYsAfhqQpihPSEqHA8k3lFrxedMTdnSlRZ0ktnt5fX0JCiCAIglCElhlt/k7J5xF0HUWH2ws1qonlmpHTE8t3VmpqaZFDTKC12jjM+9K7dZGEsNQ3ovh4ta6snq6QECIIgiAUwZK9lRATjpoGeQGgRFR5yx0mFvhtvSpgTNHhTmLGNcja9byyuiVg74lanL/YiD/clo7FRUc8PkdWOAgLtCWbj3rF8iPH/K9/cFoH/o6DE4KEEEEQBOGEnOBgyd7624RMzF9/SLMyAYVlVZj35Q+w1F+rsWOOM2Le+Js9eqiytO2ICg/FW9MG4kJDk9t8CFmSQgzOVZ+FAovDQgy44oXS0E+O6CnYeNZfLjpXMeyaVagHSAgRBEEQdlgzslhS0kNCDJqUCRDrzG6pb8KjK/bhXQ8eqqxtO0JCDG7Bx2KWJFd9IyR3rtg4jOidjM0/nte0VYZr2j4v9HyJVEaZnnrX8ShqsZGWlgaDweD2b/r06QAguM1gMOCVV16x7+ORRx5B9+7dERUVhU6dOmHChAn48ccfnY5TW1uL/Px8mEwmmEwm5Ofno66uzmnMyZMnMW7cOMTExCApKQmPP/44mpudA/NKS0sxbNgwREVFoUuXLnjxxRdBSXIEQRDCKO3snpuZgu2zc7C6YDDeuK8/Vk4bhFfv6YemKzYUH6/GyAyzx60VWm0cnllbKjnmmbWlgt3jWVDTtqPVxmHH0Qt45rNSj2JuNv94Hq/f0x9z83ojf3BXxEZ6bpvgXY18qw61nek9ITEmQnK7XnrX8Sia9T179qC19Vp1yrKyMowcORL33HMPAKCqyvlD8s0332DatGm4++677a9lZWVhypQp6Nq1K2pqajBv3jyMGjUKFRUVCA0NBQBMnjwZp0+fRmFhIQDg4YcfRn5+Pr766isAQGtrK/Ly8tCpUyds374d1dXVeOCBB8BxHN58800AbWlzI0eOxPDhw7Fnzx4cOXIEU6dORUxMDJ566iml80QQBOET/JVSr7YDPZ+9VVhWhaf/fVDQkrR9do7qayo5Xi0bbFx3qQU7j13A7Td2YtqnI0rbdghZzNRi44DH/3UA04akYUyfVGTfkITpq9wLH7LSMSYCWd0SND1HVhJjwjF37M0wx0XCYr2MJ/91UPY9egmU96iO0BNPPIGvv/4aR48ehcHgvqjvuusuXLx4Ed99953oPr7//nv069cPx44dQ/fu3XHo0CFkZGSgpKQEgwYNAgCUlJQgOzsbP/74I3r16oVvvvkGY8eOxalTp5CamgoAWLNmDaZOnYrz588jLi4O77zzDubMmYNz587BaGwrlb5o0SK8+eabOH36tOD5CkF1hAiC8BUbvq/Cc+vKnNLOfRVc6kndHDEXkRaF9V799kcs2XJcdlxMRCheu7ef4uM0X7Fh8MLvmOodKS06qBSxKs5KiHcJ7NYaMTen4z32tAaTVrA+v1V3n29ubsaKFSvw4IMPCoqKc+fOYf369Zg2bZroPhoaGrB8+XKkp6fj+uuvBwAUFxfDZDLZRRAADB48GCaTCTt37rSPyczMtIsgALjzzjvR1NSEvXv32scMGzbMLoL4MWfPnkVlZaXayyYIgvAKCzeU47FV+9weyFUibilP4N0m6w6cQfHxarTaONUd6OUsSUCbJUmt68q9J7owDc2tiuepsKwKw17ZIimCADi1kvBmcIXF2oj3t1bgL6NvknUviaFGBCVGhzONe3LEjUxuTj6rUOzOGdAm+nzZT08K1Q7JL774AnV1dZg6darg9o8++gixsbGYOHGi27a3334b//M//4OGhgbcdNNN2LRpEyIi2m66xWJBcnKy23uSk5NhsVjsYzp37uy0PSEhAREREU5j0tLSnMbw77FYLEhPTxc876amJjQ1XctKqK+vFxxHEGrQYyVhwv9s+P4s3ttaIbqdg3bBpWLB0Pf96nqm97u6kliDjdUW1svu3hFLthxjHs86T2JWLEccA7+Lj1d73dXEuyD/+tUPTKUHPMWe4XdXH8xfXy6a4Qe0WZr4xqxy32F67KcnhWqL0LJlyzB69Ggnq4wjH3zwAaZMmYLISHf/65QpU7B//37897//Rc+ePXHvvfeisfHaAhOyMHEc5/S6mjG8F1DKLbZw4UJ7kLbJZLJbqgjCUwrLqjD05c2YtLQEM9ccwKSlJRj68mZNf+kTgUerjcNz68pkx2kRXCoVDL246Cjio8MV/4pXa0liZfANHRHPaLFgDcKVsmLxJMaE479/Hm63dPgqnoWDe8q5t+CtOWP6puD5cRmS81F3qQWbyi32mLAJ/bsgu3tHUTHDZxV6EijvK1RZhE6cOIGioiKsXbtWcPu2bdtw+PBhfPLJJ4LbeZHRs2dPDB48GAkJCfj8888xadIkmM1mnDt3zu09P//8s92iYzabsWvXLqfttbW1aGlpcRrDW4d4zp8/DwBu1iRH5syZg1mzZtn/rq+vJzFEeAxrp24i+NhdUcP84PPkYcwSDM2j5Fe80mBjpYSGGLBoYh/B9Hkx5OZJzooFtImRvSdq7VYsLatp+5sZw3tgSI8kJ2vOyAyzZHyRmpT33MwUjMww694KrsoitHz5ciQnJyMvL09w+7Jly5CVlYV+/fox7Y/jOLs7Kjs7G1arFbt377Zv37VrF6xWK2677Tb7mLKyMqcstY0bN8JoNCIrK8s+ZuvWrU4p9Rs3bkRqaqqby8wRo9GIuLg4p38E4Qnej6EgAhkl4saThzGLC6vuUgueYIwD4fFFPEhuZgrevX8gc9yM3DypsWLx1+kr1MYIScHfiydH3uhmzdldUSMZX6Q25Z3VguRPFAshm82G5cuX44EHHkBYmLtBqb6+Hp9++ikeeught20//fQTFi5ciL179+LkyZMoLi7Gvffei6ioKIwZMwYA0Lt3b+Tm5qKgoAAlJSUoKSlBQUEBxo4di169egEARo0ahYyMDOTn52P//v347rvv8PTTT6OgoMAuXCZPngyj0YipU6eirKwMn3/+OV566SXMmjWLOWOMILRASQwFEXywipuOMREeiQnWh39aUrRTbaDVBYOxfXaOqMWSjwcB3MOatYwHyc1MQcmc3yAxRtpNZo4zys6TGiuW43X6grsHdpEfpAKhe9Fq47Dj2M9M79dLyruWKBZCRUVFOHnyJB588EHB7WvWrAHHcZg0aZLbtsjISGzbtg1jxoxBjx49cO+99yImJgY7d+50CpBeuXIl+vTpg1GjRmHUqFHo27cvPv74Y/v20NBQrF+/HpGRkRgyZAjuvfde3HXXXXj11VftY0wmEzZt2oTTp0/jlltuwWOPPYZZs2Y5ub0Iwhd4O4bCVwhlGhGew2ppmD8h0yMxoeThr/RXvC/iQVptHPaeqMVv+0sLhMYrNmwqt0iuV7VWrNzMFDw5oqeHV8LGVwfPIj6KLTaKhRAD8NbkAYLtN4a+vJmpRAHQvlyEPB7VEQoGqI4Q4Sl6qanhCaxtFwh1yGUwPXJHOuaM8cwa0WrjMPTlzbK9v7bPzlEtuLzZFNV1/Ym1ceBfd413cV2v/JwD8nVxHGm1cRiyaLNfGph6iut3DEvmHI8W68PXeL2OEEEQbARaTQ1XlLZdIJTDW1RcLUOJMeF4e/IAj0UQoMyFpdb65414ELH1J9XLCnCvp+O6XtVasUJDDJg3PgMGiFc4ig7X56P1/MVG+739fN9pPPt5GbMIAvSV8q4lZBGSgSxChBao/fXpb3grgliMUyD+StQzvqgzJWfd05P1T279KUVovaqdc7F5Gt8vBesOVOnSYvTkiJ5Ys+eU4vkMVOsv6/ObhJAMJIQIrdDTA4aV9uDWI9wRe/h7s1WGGljXn1K0Wq+u81jb0ITpq/ZrUn26c2wEDIYQnKsXL3LIigFtrsJaFVWnZwzvgSdH3hiQP3RYn9+et7olCIKJQKmp4Uh7CfQmnOFdWI6obbqqFhZLjLfWlVb7dZxH3nqllWWhqZXD729JwftbK0TjoVjgZ1Tt+4f0SNL1d5QWkBAiCB8i9ADSM94ulkfoB2+3ynCE1TrqrXXljf2W/KRtCw7rpRa8v7UCeX1TsP3oBdRdVldtOj46HFNvS8fioiOK3se7EfUau6gl+ozoIghCFwR6oDfBjq+sf0qC7+XWnxo6xkQgq1uChntsu6bpK9krX7PAXf339fdVdhEUHxWO3w4QbmslhjEsBF07Rit6T3sPjnaFhBBBEKL4qliep1CNI89htZJcuNikep6VVllXU8QwPkra0VHd0Ixhr2zRLNuRF3ZqLTZKqLvcgs/3n1X0Hkt9E2p+aZIf6IDS+k+B/vkj1xhBEJLwacaurgyzTgK9AzEIXY9kdUtAiAGQe4bNX3/I/v9K51mN+41ff89+XsrUk42lc4BWPf5YmrfqgeqGJpjjIiUDrxNjwjF37M0wxymLXWwPnz8SQgRByKLXQG9qZqsde0/UyoogV5TOs1r3W25mCi632PDkJwdk38uSGSUW/K00lZ6leaseePs/PyE+Otx+3UIlPF76bR/Fn5X28vkjIUQQBBN6C/T2dZZTe0HsYa8m9kfpPHsSfG+O0zbAmbc+fbijAkmxRlReuITVu0861f+Rs2xYrJc1PSdvYr0qEE0uFbfVWnbb0+ePhBBBEAGJL7Oc1OKLAolKkHJjqM2kUjLPfPCzXJsPoeB7ufeqxdHV54qcZaOmoVnDM/EuvDiJDAvByocG4cIvTR6tSSWfv1vTE3X1OXCFhBBBEAGJ3mscCYkOc1wkJt3aFWlJ0T5/IMi5Mf530gCmGCExWOaZD37+44p9oi4aseB7qfd6CznLRmIHo2bH8mTuWeHQFjwdYjBgwtXmtXygs1KRwvq5Kiq3YNa/Dug6hoiEEEEQAYmeaxyJio76Rqd6Lr56ILC4MeZ9+YNHD2LWefYk+F7svd5EyuKVHOu5EOIlx5JJA3D0/C9YXHRU1T7io8PRfKUVDc022fG8iPEk0Jn1fi/bUen2mt5iiEgIEQQRkHjiZvEmSjKJfPVAYHFjVKt086iZZ0+C7x3fa6lvxPyvf2DKJvMUXjzw7s6icgvW7j/t8X55ATgyw4z5L29W/H7eOqakfUZybKTHgc4snz+DiJVLbzFEJIQIgghIPHGzeBMlmURaPhCk4pG85R70ZJ49Cb7n31t8vNonIghoq580/6sf8PmBMx4f0wBg0qDr0T2pA/Kz0xARFoLi42yVqWOMoWhoarX/bTZF4nJLq1MAtNRxzaZIZHVLwK0vFXkU6Cz3+eMASHUy1UMMHw8JIYIgAhY91jhSKjq0eCDIuThY3RiJMeGobWgRtWa5xrH4u5aUr+K/QgzSQdVK4QCs2nUKAPCP7RV4flwGmq7Iu7QA2EVQfFQ4/jAkHQOvj0f+8t3Mx35+XAbe+c8xSeHEuiZzM1Pw1uQBeG5dmZM4NJsiMTrTjA8E3GKu6KFPIQkhgiACGr3VOFIbk6T2gcDi4hiZYWZyI87N643pq/aLWtiWTBqAhBijLuYZ8F38lzeDmPn79MSInoreZ73cgsVFRxAdEco0Pj46HIsm9sHIDDOe+ayU6T2ua9LV6ljb0Iz56w85iaDEmAjMzctAQkwEkxDSQ59CEkIEQQQ8eqpxpDbNW80DQUktFxY3Ym5mCt4JMejKwiaFt1LqeXyVyWUAsHr3Sdnqz67vA4BLza2S43jemjQQQ3omofh4NXM7EMc1KWR1FKK2oRnTV+3DW5MH6jKGTwjqNUYQBKEhUv3ZhPCkca2SWi68G9FschZcrn2lcjNTsH12DlYXDMYb9/XH6oLB2D47R3ciCGDrhacGA4BnR9/kdRHEw6e1T7q1q/34WsGvr8FXfyiwWh7jo8Pta1KsUa4Q/JTNX1+OuXm97efgek6APvoUAiSECIIgNEdMdLji6QNBaS0lVpHDW9gm9O+C7O4ddfGwEkNK4L09eYCq7vUcgENV9ZqdIytdO0YzrRulOK4vVsvjH25LR2iIQVU/NV6AJ8QYmcS3vyHXGEG0M/RWzThYcY1dqrzQcLWFw7VO4J66nNTUUtKTG5HH0zUrFScWEmJQVYTxlyY2l5OW/HVdKV6e2BfbZ+dgd0UNdhz7GUu2HFe9v/iocCy627mHGIs7MT46HDNyegDwrJ/a+YuNmNC/i65i+IQgIUQQ7Yj20Am6PeEqOmbk9NT0gaDXWkpK0GrNigk8tUUYk+MivBp/JMTFxlY8tmo/HrmjDnPGZKC2oUn+TRK8NWUghvRIcnrNMe1djEUT+2hSeoEX4HoU346Qa4wg2glifnw+K6WwrMpPZ0bwaO1yYomR0UschhC+WrOOLsHJg65nes/A6xMUxXppyXtbK/DVwbOqU/btcUE3CIuP3MwUPHxHOlyXRYgBeOSOdCcBWnnhkurj61mAO0JCiCDaAXLZQ0Bb9lCrr6I/Cc3ge0GtO3AGxcer3e4haxC03vD1muVF6Li+XZjGpyZE2+fWFB2uyTko4c+fHvCojcjcvAzsrqgRXDeFZVV4f2uFWzA4xwHvb62wC9DCsiq87tAShoVAEOCukGuMINoBgdCJnVAOq9tIb7WUWPDGmmWJNeLdiVLHdrRmjMwwY96XPzAdX0sar6gXgL/pnXy1yOG1tinmuEjMG9/WyoOl5ELOTZ0VB0kDyuLe9BLPSEKIINoBeu/ETihHaS8ovcdhuKL1mmUVjY4xMmJxVY7WjLaeZp7F6viaokPn3V6z1Dfi0RX78MRvejAJ0I+LK5ksUjN/0wODb0hSLGb0FM9IrjGCaAfouRM7oZxgcHVquWaVxhrxLq8UF3diios7sdXGYcexC0znGSi8/t0xpnEnathig27o1EFx3Jve4hnJIkQQ7YD2kD1EXENPrk5vuS+0WrNKqms7nrecO7GwrArzviyHpV65FdUAIK9vCsZkmjF91X6fZZ1pSbfEaKZxSn9cqb1f3oSEEEG0A/TaiZ1Qh15cnd50X2i1Zj0RjWLuxMKyKjwqkV4uxV39U/H//a4fIsLaHC7vhBjw7OelHnes9xW8AM3PTsM/tlcwtdRQgp5EPg+5xgiinRCo2UOEO3pwdfrCfaHFmmUVgzuOXWByJbbaODyzlq0pqRC//1VXuwgC2oKt+dYZgQCHNgEaERZib5Ehxfz1yly0ehH5jpBFiCDaEYGYPUS4429Xpy/dFyMzzIg1hqP4pwsA2iw0fP2b4uPVsuuYVQwu2XIMn+07LWvNKvmpGnWXlFtvhO4Ja6NSPREfHY6RGWYAQEKMUXZ8lbURSzYfxcwRNzLtXw8i3xUSQoTH6CUFkmgj0LKHCHf87er0lftCSCh8tu80xvdLwZcHq5hccremJ8IcZ2TK7KqytmVOvT15AMb0TRUcs6LkhOLrELonYll/3iTEAI8bxdZdarHfV1arzOKio+hljmWy4Plb5AtBrjHCIwrLqjD05c2YtLQEM9ccwKSlJRj68maqYuxn5IrwEfrHn65OX7gvxFxvVdZGvLfVPTZFzCW3qdyCxis2RceesXo/Nnzv/h3VauOw9cjPivYFuN8TNY1KPeV3A7tgyaQBmuyLv69KrDKsWYx6rIZOFiFCNUrrnBC+QUmAayBa8wLxnJXCX2PTFRte/V0/wABc+KXJ6Xq9OQ/edl+o7WhuAPCXz8twubkVZlMUahuaMX2VcquLjQMeW7UP74Y4f0ftrqhBQzNbs9W/jOmN5Dij4Nx70qhULf/edwYdO0QgPjpclWvPEf6+shSf5FFiIRTr/+ZpE2K1kBAiVKHHFEhCmTjVU0EzVgLxnJUidY38Q8bb86DGfaFEmKkVChyA6oZmPPmvgwDaXEGeWF1cv6NYLVzREaF4cGi66PX5q3Dpe1srPN5HiAHI6pYA4Jr1hjWDTsl16ymekVxjhCqUxBAQvkFJET69FTRjIRDPWSks1+iLeVDqvlDqItdKKHjq8XX9jmK1cD1yR3fJB3YgFy61ccDeE7X2v3MzU/CkxoHQvOv+6+/PAgDG9k3VpAmxWkgIEarQYwpksMMqTkuOVwdc1eJgqLTMco3zvvwB8770zTywxiipEWZ6EgqO31G8JUzqcRwfHY4ZOT0k98myHz3DzwkvWLp2jEaCTOPZEANbTSE9xpWSa4xQhR5TIIMdVtFZ/NMF3RU0k0OPRdi0huUa5TKjtJ4HOfeFWhe5nOvNlzh+R0ll6/EsmthH1nKh1KWkN5JjIxWn/ts4YPqqfXgnRDw2VK9xpWQRIlQh94vHAOcOzoT3YRedbL9T9WTNCwYLpJbn7um+mq/YsGzbT/jrujJ8uKMCWd0SBHtJqXWRS7nefIXYd5SYJSzFFIl323kCCD8ntQ3NglY+FsQsknq26pJFiFCFv+ucEO6wBrhmd++IJVvkGy/qyZoXDBZILc/dk30t3FCOpdsqnOJvFmw4hILb0zFnTIbTWE8EqljmUIopUrCOkJbIfUdJWcJYgsL5h34gwV/B3LzeePFrdan/UhZJPVt1SQgRqtFbCmSwwypOB9/Q0ecFzTxN9dZjETat0cJd5Ok8LNxQLph5ZOOuZSQ5iiFPBaqU4Pif3N7YXVEDS30j5n/9g0e9ulwLDbJ8RwkVJpXK1nO8jgsXmwKqmjQAJMZEYMFvM3HYclFVo1lHhISvnq26JIQIj9BTCiTBLk59ac3TItU7GCyQjtfIgtbz0HzFhqXbpNOvl26rwFOjbrL30tJCoIpVQnd8PSo8xD4vQtf80O3pgufOb18yaSASYiJUfUfxIn5TuQUf7Kh02265Wq1ai/o9/uS5q33FFhcd9XhfrsK31cbhwkX5yt9C7/UFBo7j/B2rpmvq6+thMplgtVoRFxfn79MhCCZYLDC+qMkjFhzJn4nS4MhgqCP0RtFRLC46IjsuMSYCNQ5ZOp7Ow7JtP2H++kOy4+bm9ca022+w/83fY0BYpGgVACt3772xNjZ8fxbPrSsLmM7xnrBy2iA8/e+DHlmyeOG7fXaOU2kFlqBrofd6CuvzmyxCBNEOYek35m1rnjeKbgaDBTItKZpp3Ny83jCbojSbhxM1l1SN85WLXO7es64NVjetmJuwPdLmOuQ8FkGAun5r/rbqkhAiiCDGmw1avRUc6ck5B0J7DlbXgNkUpem9uy4+SvU4XwlUuXsvtb3VxmHJ5qNYvqMSdZevWXiErEYbvq/yiwiKjgjBpWZlfdO0wMYBuxQUv42JCEVsZJhTOQdX4aukjYq/40pJCBEE4RX0FhwZKG41fwWGc4wh2mLjvCmqPaWwrArPrC0VjOHhO9I/OaIn/vjrHthTUYP/+eygH84SeHx4Tyz69rBfjq2kWUlDcyve/3+3IMRgEBW+rG1U5ub1xtQh4u1KfAHVESIIwivoKeU9kNpz+Ks795k6NkHKOk4v8PdeLpB5cdFR3DT3G0xZtgu/NLE1XmWlSzzbGv/ux/OaHlcJ2TckIcXE/lksPl6NW9MTBetLAew/cJJijX63ypIQIgjCK+il6KaeC7mJwdreQku6JbLFJrGO0wNKu9x7YwlER4Qyi8c9Dj2+fEmKKRKDu3e0C3AWlmw5JtkaQ08/hOQgIUQQhFfwl2XDlUBtEJybmYLts3OwumAw3rivP1YXDMb22Tlec+PlZ6fJVnk2XB3nTfj+VusOnEHx8WqPBKraLvdacqlZW+uSNxjfLwWhIQbkZqbg7ckDwPqRdLWoOt47m42DOc7/P4RYoBghgiC8hh6KbuotVkkJvoy7CQ0xIDoiFA0SD+5oY6ikcPU0GF3rOC493lM98v7WCgzomoDczBSM6ZuKJTDgsVXy9awcsz9tNmD+eud7Fx0Rah+j59pfJIQIgvAq/k55DyQTvT/ZXVEjKYIAoKGpVTTLz1MR442GnMF+T5XgWMpiTN8UvBvi/gNGCN6iKiSc7NYwFyXk7ywxV0gIEQThdfyZURQM7Tm0wBPLmZiI4TOy/nBbGkbdbBYVwN6oOQW03XstKz53MIbhl6YrmuxLTwiVsuB/wCzedBhLthz3bP9Xb+y0IWkYkSG+DvwFxQgRBNGu0Uuskt6pvNDANC6pg9Hpb5aA5OU7KzFpaYk9uNY1Dqjkp2pdx3HFR4Vj5UOD8M6UgX45vq9wFbmhIQYM6dFJs/1vKLPoTgQBZBEiCCII8GWsUiAUbXSl1cZh9e6TTGOf+tcBzBt/s33OlAQk8xYiVytNfFQ40/uVxvzsrqjx2BpkALDo7j4Y0iMJO45e8GhfekfIlahFM2Aef3WXl0ORRSgtLQ0Gg8Ht3/Tp0wFAcJvBYMArr7wCAKipqcGf/vQn9OrVC9HR0ejatSsef/xxWK1Wp+PU1tYiPz8fJpMJJpMJ+fn5qKurcxpz8uRJjBs3DjExMUhKSsLjjz+O5uZmpzGlpaUYNmwYoqKi0KVLF7z44oug1moEEZz4IgursKwKQ1/ejElLSzBzzQEnK4ieaevyztYU81x9k1OmkJqAZFdx4ljpWQqlMT9aBEs/MeJG+xopOmTxeH96RCqDi8WiqgQ9BrArsgjt2bMHra3XgunKysowcuRI3HPPPQCAqirnD/s333yDadOm4e677wYAnD17FmfPnsWrr76KjIwMnDhxAo8++ijOnj2Lf//73/b3TZ48GadPn0ZhYSEA4OGHH0Z+fj6++uorAEBrayvy8vLQqVMnbN++HdXV1XjggQfAcRzefPNNAG3N1kaOHInhw4djz549OHLkCKZOnYqYmBg89dRTSueJIAQJxF//esSb8+ire7Th+7N4bNV+t9c9Cfb1FUoeTq4xO74ISFYbx6XFuaUlRaPVxuF/vzuKD3ee8Hh/eoQDMDdP3D0sZVGdm9cb89cfYrYK6jGAXZEQ6tTJ2Ve4aNEidO/eHcOGDQMAmM1mp+3r1q3D8OHDccMNbZ2KMzMz8dlnn9m3d+/eHQsWLMD999+PK1euICwsDIcOHUJhYSFKSkowaNAgAMDSpUuRnZ2Nw4cPo1evXti4cSPKy8tx6tQppKamAgBee+01TJ06FQsWLEBcXBxWrlyJxsZGfPjhhzAajcjMzMSRI0fw97//HbNmzYLBQA8rwjMCpWWD3vHmPPrqHm34vgozVruLIMCzYF9fofThxMfslPxUjcE3dESKKdKr9Xo4qIvj0sKtU3mhAVl/26RZwDUL0RGhGH1zZ3y2/6zPjjl/fTlCQiD6uZDK/gwJMcg2V9VzUoLqYOnm5masWLECDz74oKCoOHfuHNavX49p06ZJ7sdqtSIuLg5hYW2arLi4GCaTyS6CAGDw4MEwmUzYuXOnfUxmZqZdBAHAnXfeiaamJuzdu9c+ZtiwYTAajU5jzp49i8rKStHzaWpqQn19vdM/gnAlkFo26BlvzqOv7lFhWRUeW7VPsiqxv4N95bg1PRGJMWxxOo5MX7kPm8otiioS+xJHt44aOhhDsbjoqE9FENCWdv7Z/rOIjw5njp/yFD5+S+pzwWd/urbVyM1MwcN3pEPMvqD3pATVQuiLL75AXV0dpk6dKrj9o48+QmxsLCZOnCi6j+rqasyfPx+PPPKI/TWLxYLk5GS3scnJybBYLPYxnTt3dtqekJCAiIgIyTH83/wYIRYuXGiPTTKZTLj++utFxxLBSSC2bNAj3pxHX90j/jis6DE+Amh7wL047mbF76u73II/rmirH/P25IHMFYnVoPZ+5Wam4C2V56Z1zzGl1F1qYY6f0opn1pYqnufCsiq8v7UCYiG4puhwXbuGVQuhZcuWYfTo0U5WGUc++OADTJkyBZGRwibX+vp65OXlISMjA88//7zTNiELE8dxTq+rGcMHSku5xebMmQOr1Wr/d+rUKdGxRHASqC0b9IY351HJvj1p6aC0hYMe4yN4Onpwbi98VY47M81YMmmAhmfkjCefqYSYCK/0EWuP1F1qwZLNx5jHs5RPiAoPxcgMs8QI/6Iqff7EiRMoKirC2rVrBbdv27YNhw8fxieffCK4/eLFi8jNzUWHDh3w+eefIzz8munPbDbj3Llzbu/5+eef7RYds9mMXbt2OW2vra1FS0uL0xhXy8/5822dfV0tRY4YjUYndxpBuBLILRv0hDfnkfU9ReUWzPrXAdUxRErOTS99lcRQu14dReWYvql4N8QgWpE4IToctZda3FousGKxXgagPACe9drCQw1oaSXFtHTbT+iaGAWzKUqyCObuihrsOPaz7I8BvabN86gSQsuXL0dycjLy8vIEty9btgxZWVno16+f27b6+nrceeedMBqN+PLLL90sRtnZ2bBardi9ezduvfVWAMCuXbtgtVpx22232ccsWLAAVVVVSElp+7LauHEjjEYjsrKy7GOeffZZNDc3IyIiwj4mNTUVaWlpai6bIABQywZP4b9Aj567yDRezTyyvmfZjkq31/hYiSdH3IgZOT0kH7BKzk2v8RE8nq5XXmw4BtVarJdR09CMxA5GmOMikdUtAe/85xiW76h0cvnERYahvlG+YnNNQ7OqAPikGLYft+1RBEWEhaD5ik3Re35puoIn/3UQgPDcCt0DOfT8w1Cxa8xms2H58uV44IEH7AHOjtTX1+PTTz/FQw895Lbt4sWLGDVqFBoaGrBs2TLU19fDYrHAYrHY0/J79+6N3NxcFBQUoKSkBCUlJSgoKMDYsWPRq1cvAMCoUaOQkZGB/Px87N+/H9999x2efvppFBQUIC4uDkBbCr7RaMTUqVNRVlaGzz//HC+99BJljBEew2eiBEJXZb3hWGdHrmy/J/PIco/kNMnioiMYsug7yeBRuePg6nHenjxAl/ERTt3COQ7mOKOq2jCAs5Dig2p/O/A6TLv9Bvx2QBdYLzdj2Ctb2oKPr4qg+KhwPDniRjzPGJ90uu6y4gD4wrIqPPXpQZVXFfhk39ARBqir+QO4z61YEoIcev5hqFgIFRUV4eTJk3jwwQcFt69ZswYcx2HSpElu2/bu3Ytdu3ahtLQUPXr0QEpKiv2fYyzOypUr0adPH4waNQqjRo1C37598fHHH9u3h4aGYv369YiMjMSQIUNw77334q677sKrr75qH2MymbBp0yacPn0at9xyCx577DHMmjULs2bNUnrJBOEEtWxQh5IvUE/nUe4ecQBTzIjFpXigkuPwLJk0EGP6CsdS+hPX4o9T/rELjVds9nR/VlgE64bvq/CowL23Xm7B60VHcLr2MtOx1h04qygAnl9zlnr9WiO8zR09k/DO/QNhNqkTIo5z23zFJhsP5Eog/DA0cFRqWZL6+nqYTCZ7mj9B8FAdIXZabRyGvryZ+Vekt+sIjc404wMBt5gQfP2T7bNzREVZoK0FsSapvEh0bYEhFtvDz4ZURtCG789ixur9osKTn1+O4ySrWyfGhKOmQT6DanXBYGR378i85kyRYbAyuOUCEQOAw38bjYiwELtLekPpWXxcwtZOxRW+eKKS4wPS68ObsD6/qdcYQahEqsAY4QxrdtWM4T0wpEeSZvModo92V9QwCyGhztysx9HjWmDp9B4VHorpY7rjVO1ldEuMRn52Gjb/eE5xr7a2GkvChSYdj1llbcSTI3ri9aKj9td4+Bn8bf8ugjFdrvCxKMxrLqcnFmxgf7gHEmP7mhER1ub44d2VAFQLoRM1lxSN90YvP29AQoggPMDxy4UQhzVQsmfnDprPp9A9UlNxWO4aAmUtsJYWWLDhR/tr/9hegefHZWD77Bxmsae0xlJaUoxkY1xTVASTEOJjUVjXXN2lZsRHhfu8Xo+3MYaF4PX7Brq9zq99NZXAuyVGM43T+geNtyEhRBCE19Fbph0f28MXA2RBz8GeSlCTvaOmX5qaGkvZ3TuKWtZabZykeHVt4cB6v976j3TQPgBEhoegsUVZ5pW/uWKz4duyKsH4tPt+dT0WX7W+sZJiikR+dhr+sb1C9h48OfLGgBBAPKoLKhIEQbCS1S1BNksrxNA2zlfwjSTNcdIPzEAI9lSCGkGnphq30hpLNhuHdQfOYHdFDW5NT3Rr46A0SYElo0+OhOi2GneBJoIAoNUGPLZqPxZuuGaV4wPklYogABjfLwURYSHtMlGEhBBBEF5n74la2SwtG9c2zpfkZqZgxzM5eHJET8HtgfzlLoZagaC00rcSwXW5pRVTlu3CzDUHMGlpCYa+vNmeqddq47Dj6AW8+u2PKDtTj5m/6YnOLuLVbIp0s1axZPSJER8Vjo8fvBXGsMB/RL63tQIbvq+SzdrsYAyV3M+XB6vQauOu/YAwyd+DQIFcYwRBMKO0oi+Pnqtxh4YYMHPEjehljlUcDByIqHELOsJ6j1jisAwGgOPg1tSUd8U9fEc6Pvm/027bTVFheHJET6QlxUiuQ/6hrbT4X93lFhw5d1Eyiy2QeO6LUkSEhUrGw8n1VXNMGAik5AAWSAgRBMGEJynieosREqK9fblLwQuEZz4rVRwkzHqPHAWXWEuNuMgwWC+7p67zY9/bWiG4b+vlK1hcdBTv3j/QLUDdVayPzDDDZuPw3LoypvR7HqUZUnqm5lILAM+DwR1FcKAkB7BAQoggCFnE6s6wBtHKWQdcA139RXv6cpcjNzMFscZwTFm2S34w1N0jMYtMiilSVcCuKy98VY6RGWa7WBUS6641kVjpEt8+guO1REwEC1mKAQTMjwoSQgQRBKh1afHvlas74/pAckXKOtAe43AChcHdOzKVEfDkHolZ2r7+/qzq8+ZxdNeIiXU1IggA3vuvsDUqGJESwWLiE3Ceez0XGCUhRBACeCIc9IanVY9Z687IdZcWsw60xzicQILFMuPpPRKytGnlBj1/sVFSrKul5lKzhnvzP4kxEahpUH5NUiJYifhUU4LBV5AQIggXAq1dghSeurQAbQOdgykOR+/IdRBPjAnHb/t3wYgMs9M90upHgpqilkIkx0YqrlkUjHQwhqKmQfn7zKZI3Perrmi6YkPx8Wqnuk5KxCer9dgfkBAiCAe0EA56QQuXFqBNoHN7srC1B8TWOc+TI27EjJweghYALX8k3PerrlhcdETx+xyPfWt6Ir48cEb1PoKFkzVsjW2Btp5iiTER2HHsAjYdOud0j1IcKn0rFZ+s1mNfQ0KIIK6ilXDQC1q5tDwNdG5PFrb2gNwveQOANXtOYkZOD6fXtfyRIGeNYuX5cRnYVG5R1AiUkCYmIhQppig8+0WppIvrwSFpqo/hjzIZUgR+tSiC0AglwiEQ0MqlpbSiryNiRdz4L1O+aB7hO9Ssc7kfCQB71Wm5wn4sJESH49372/po/XHFPlWxL0pIjAn36v71RENzKx5btU80yJy/w597YIXTW7saEkIEcRU9F/1Tg5a1e9RUk9Xy4UloB+v63XHsZ6w7cAbFx6tR8lO1Jj8StAhqTogOx4K7MjEyw6xoX3wmk1JiI0OxY/ZvsPKhQYiPCh5BJAUHoKahxd6CRAkJ0eF+L5PhCrnGCOIqgVD0Twla1+5RGuislWuO0BbW9btky7VmpKwCQE5kaRHUXHepBdNX7ccTI35h2ldiTDhe+m0f+9q1WC9j/vpDzFaki42tyHntP3h+XAYW3d1HMrYq2Gi6orwHmx7njixCBHEVuR5MgdZ80xOXltQ+s7t3dGuIKUR7s7C1F25NT4Q5zqjoPazVp+VEFuu9nj68u6j44h+ky3dUMu1r0q1tGU98M1ezKUqxK4135QLAO/cPVG1dam9capZuyyFE3aUW3YUXkEWIIK7SHov++bN2T3uzsLUXvi2zyPaVUgqrdZH1XidGR0iKLw7s4uwtB8tWiikSYzLNTO9zPR6fLPHfPw9HZFg5tGhZEazo7ccPCSGCcKA9Fv1zdWkldTACHHChocmpLojWBEpbjfYAa3mChRvKRft3qUXJjwTWNZEYE8F07PiocFgvtzC7WyzWRixjtCS5wrtyPy6uhKVeXw/yQKPygr76uJEQIggX2mPRP96lVVhWhac/PeiTVHZvW9hcH/5Z3RKw90St7u+Z1jWVWMsTbPj+rCYiKD4q3Mkao+RHAuuaMEWxCaE/DEnH60VHRJu6usKPCbna9V5NvEp7asaqBmNYiKrYIEdeLzqCXuYOuvlhaeA4To+xS7qhvr4eJpMJVqsVcXFx/j4dglCNWB0Y/gHkrWKR3qgjJLTPEAPgmICmx1pFWs8F6z1ttXH41YJNirqvi7Fy2iCEhBg8EnJy89Bq4zD05c2ylqPts3OwqdyiuiYRq4ByZG5eb6/WLVJzTr5k+vDuTu5GNTjeP2/+WGF9fpMQkoGEENEe4B8sYg8Lb38xaWkFkauKzONtgacUrYWoknu6u6IGk5aWqDpvof1psUbk1gQ/X4Cw5chxvhz3dfTcRaeMNzEeHJKGb8oskmLaEf76Nz/1awx5ebPXahdJnYMeWDltEJ7+90FJkRpjDMMvTVdk97W6YLBXM0ZZn9/kGiOIIMDfqexCTTfVoKQOjZ6qgXujarmSe6o0poXVlemJwJVbE0ri9Rz3VXy8mkkIjcww4y95GU7nX9vQjOmrxMXX+H4pyHntP14t4KhXEcQLwcHdO8q6N++95Tp8wBCLpZegaRJCBBEEtJdUdqV1aPRSq8gbQlTJPa35pYlpbFxkGBZN7IMXvz7kJJ6ExIcvWqeoidfjywNY6oWv2TFIX0iMvRMiLL7G90vB+1srJEV4ytUGpdbLzfjiwFknwdTBGKp5tp4v4dAmBENDDLIi1RQVwSSE9JIxSkKIIIKA9pLKrlao+VvgeUOIKrmnNkYzw90DuyAkxACbzfmB3drq/LcvmxMrtSZuKregUSSYl5dPc/N6i4orIfGV1S0Bw17ZImuJbGhsi8H6S14G/pKXgSWbj2L5jkrUXW4JaBHE8/7WCgzomoDczBRJkdpq4wIqY5SEEEEEAe0llV2tUPO3wPOGEFVyT1kL2MVHG/Ho1bgcR87/0oJHV+zDu/cPlGxt4W93pFz8mCk6HL+/5TrMX39I0pLlKr6Kj0u3GOGpb2rF4qIjWL6zAr+/5TpZC1Ig4nhvxURqoNVko8rSBBEEeKPKtD+Qq/7til6qgXujarmSe8ofXwpznBHvbZWOrZn1r4Oa9R3TGtb4sfe3VihuAqzUolh3qQXvtUMRpOTequlP6C9ICBFEkBBIX0xiSD38XdGTwPOWEGW9p/zxpYTYPbdcJ9sy4VJzK3YcvcB0br52R7LEj9VdEi6+6NgEuPmKDcXHq+0NZ1ttnN8tinqD9d7mZqZg++wcrC4YjDfu64/VBYOxfXaO7r5ryDVGEEGEFsUitS4IqBSxQE3XtGN/VQMXmx9vVS1nvadix+fdQt+UCltDXDl4upZpXHJspE/XiqfCi7d2DF74nVOQc4opEnPzMiTdkMGGEmGoVcaoN6E6QjJQHSGCuIYvMoVY0WNlaZb58beQFDv+797Zif87IS9ysrrG46y1UTY2iS886Ku1Uny82uNaSULwd+bhO9LbZcyPUkIMwI/zRyMiTP8OJSqoqBEkhAiiDX9Vpg4UAn1+nlizH18cOCs77q7+qegcZxRt12GAuGjw5lzIVaP2BEdx51paIBiZMbw7hvToZI9p02s7IhJCGkFCiCC8V5na39YRrc7F35W7tWDbkZ+R/8Fu2XGjMpKxsfy86PaC29Px9fdVflkrUtWoOQDx0eGwisQJsTBjeHdk35CEPZXVeP27Yyr30n6Ijw4H0BZ7xaOn1jYkhDSChBBBsLsdlJTM15ObzdNz8cb8KEELQdlq49B33rdokAmYliMxJpypp5m31orU2P0na7VpPBsdjuYrNtng8mBETxZQarFBEIRmaF0Q0JcF+XxxLv6s3K2VoAwNMeD+wV09FgqsjV29tVbEgsc3lVvwvgYiCHC2gBDO+LuWlBr0H+1EEITf0bIgoFzfLaDtS7TVB02XtDoXf1Xu5kWC0ro4QrTaOHx5kH28p/Bz0Wrj3NLVHc9Jzf3hM5Um9O9itzo9v+4HSZdYiEG+JAPBhr9qSamFLEIEQciiZWVqbzaAZXUR8eN2HPtZk3PxR+VurRu5Ku3jJkViTARqG5pl50LImhUfFY4/DEnHjJwemq2VmWv249xF6X5rem126ms6MHaOZ8HfrW1YISFEEIQsWpbM95YbidVFJDTO03PxR0sBrQWlFg+ta9lVGZi+SnouNpVbBF1edZdb7G0q7h7Qhem4Uue+cEM5vv7ed5auQEcrEQT4v7UNK+QaIwiCCa0qU3vDjcTqIhIbp8W5+LpytxJBKeR+cn0tqYPRo/NxFDlj+krPhVS/Mp66Sy1YxtDBHBC/P81XbFi6TZu4IIIdvbS2YYUsQgRBSOLqbvrvn4d7VLhQazcSq4so56bOTL2oPDkXLSp3s8IqFCsvNLil9gulPZvjjIiPDlcdCOxaIVtqLlibmAJtsTscB1Vr5ePiSnJ5+Rg9tbZhhYQQQRCiSLmbJvRnc1u4orUbidVF9HFxpSJLkNovdF+1FGARlPHR4VhcdNRtm5DYOVffpKq+zrQhaRiRYXYTfFLxWkXlFub980JGzVo5UXNJwZUQcsRHhaPu8rW1ExMRikstrXAswmMwtNWS8nfqvBJICBEEIYg3U9y17LvF6iJS+lDkY11MURFYd+CMXws+iokKMUHJo0TYKBVBIQZgyaSBGNPX/V5JCWgAzC4vngeHpOGbMovitXJ9QrSi4xDSvDVlIEIMBpy/2IjKCw2CItvGAe9vrcCArgkBI4ZICBEE4YbWGUlCaOVGYnURdUtkeyjOGN4DQ3okobahCfPX+7/go1wQ+Dv3D8Qza0vdrDwxxlCv1rtZMmmAqAiSEtCmq245JYzMMOMveRmK1kphWRX+se0nxcfSM9ERIbjUbPPLsVNMkRh8Q0eEhhjsldSl4N3ReypqUPzTBQBtllJ+H3qChBBBEG54M8XdES3cSKwxR/nZafjH9grZcU+OvBGbyi2Yvmq/3ws+sljlAGFX1y9N3ql6HB8VjkV39xG8fpa6P0rEmWMMkNhacbSWJXUwAhyw+cdzzFanmIhQXGpuDYhmqpeabbijZ0dsPVrt82M7uiBZvx8Gzt/klIW2ZMsxREeE4pE7umNGTg/dCCISQoTf0FOfKcIZf1ZKVgprzFFEWAjTOABet4YB8uuf1Srn6y5Jb00ZiCE9kgS3aVmLiCUGSE0pBJ6Qq7EsA7omiK4JPYojX4sg3gXqKHxZP/dCqfiXmlvt5REWTRQW1L6GhBDhF/TUZ4pwx1+VktXCGnPEMk4uo0kLaxjL+mf91e1LQgzAr9LEM+i0FMZyMUBi1jJWPvrDrbj9xk4AILomsrolBH0Noj/l9HBzgWrxua+71IJHV+zDuzroSUZCiPA5euozRQjjj0rJnsIacyQ3ztvWMNb1rwdrmys2Dth7olZUAGrxgIyPDsdbkwZicHfxWBIpaxkrNZea7f8vtCayuiXgjv9POg4mGLihUwe31+S+H5Sgh55kVFCR8Cl66jNFiMO7mwD3/kt6rBPCFwf8+vuzAICxfVORLfEgde1F5TjOm9YwJetfS2tbYkyE098J0eH2WkI88VFsQcxSAo1/QIqtCsPVY/P/77rNAGDRxD4Y0jNJcm1p4YJznV/XNbH3RC0s9dJtOYIBoXUo9f2gFD30JCOLEOFTfBWES3iOlinu3kRrN6s3rWFK1j/reXAcJ1oDiB8jVASTPx/+NRvHYco/dsleg5RAY4nXWjixDwB4tK48sZax3j89WuR8jVR1aLHvh8SYcNQ0KMtW9PdckxAifEogBeESvq2ULIdQcLFYvypP3Kze7BumZP2zngcApkBxoR8Wjq+12jjZytIJ0eGyAoJVQIutK5YkCrXWMqn753pcT1uOtAfk1rmUS1GJNc3fsYYkhAifEmhBuITvKiVLIWT1McdFovGKcNqzp9ld3rKGKV3/rOfhK8sdq8OaRUALrStW657aGJWEmHD8bUKm25wIr6+2liPWSy26zB7zJkKZYmII3cd542/Goyv2yb5XL7GGBs7XuZcBRn19PUwmE6xWK+Li4vx9OgEPX4hLzty/fXaObuJPCP/iaXbQ6oLBqoWc1iUe5NY/wD+EBmBM31RF5+HpuRYfr8akpSWy44TmU4t5ErvP/F5crXv8eEBZmrursJI6Lifw/8HA25Od158YUve9sKxKsNAnj9h91RLW5zdZhAif4k23AxG4iH2hapEd5ImbVWtrmOP6F8PGAdNX7cc7IQb7A4LlPDw9V7Vuay1itNRUMhezlslR5eA2HZlhlg1ejzGGokNEGM5dbD+B0ymmSIzvl4IvD1Y5N+ONCscfhqThTob7JnffeatgyfFqrNxVif8evYAGhyKfeoo1JCFE+JxACcIlfIPUF6opKkLz7CA9YIoKQ91l92Jzjvg6rViN21qrUhhqkyhcXXB8ZenzvzRh/tc/iAbtcgDmrC1FrDFcdn01NLXCGBaKJ0fciJZWG5ZsOSZ7PXrkrv6pGNYrGea4az80/ie3N5ZsPoblOypQd7kFdZdbsLjoKNbsOSVbw0nI9eV630NDDBjSMwlDeibpuoAuCSHCL+gpCJfwH3IP0j8MSVO9b73EHzjC6ubzR/ak0mw5VitOrDEcFxqaJD/jniRRCFnCio9Xy2Yu1V5qwRvfHWE6bk1DM14vOoKZv+kJU2QYrI3SIlaPfHHgLEp+qsa88Tfb78GmcgteLzqiSMi22jg8s7ZU8BhSsXl6iDUUQ1EdobS0NBgMBrd/06dPBwDBbQaDAa+88op9H++//z5+/etfIy4uDgaDAXV1dW7Hqa2tRX5+PkwmE0wmE/Lz893GnTx5EuPGjUNMTAySkpLw+OOPo7m52WlMaWkphg0bhqioKHTp0gUvvviiz8vRE+JI1XIh2j8sNXXWHTjr0TH05GZV4+ZzfPDztZLWHTiD4uPVmtfaUlo7itWKM2XZLsxccwCTlpZg6MubUVjmXqlZ6ySKTeUWpnG7K2uZxgFt1/P6d0cDUgTxWOqb8OiKfSgsq1Jd023J5mOSmYWOIj5QUGQR2rNnD1pbr/n4ysrKMHLkSNxzzz0AgKoq5wX+zTffYNq0abj77rvtr126dAm5ubnIzc3FnDlzBI8zefJknD59GoWFhQCAhx9+GPn5+fjqq68AAK2trcjLy0OnTp2wfft2VFdX44EHHgDHcXjzzTcBtAVJjRw5EsOHD8eePXtw5MgRTJ06FTExMXjqqaeUXDZBEF6A5UFa3dCMxJhw1DYoz9x5+I50UdO+P8z0aooA8g9+reJwWKpus7qt1cReiVkatKzd1Grj8IWHArq988zaUsRGSrsFhaySrTYOy3dUMB0jkEqgKBJCnTp1cvp70aJF6N69O4YNGwYAMJvNTtvXrVuH4cOH44YbbrC/9sQTTwAA/vOf/wge49ChQygsLERJSQkGDRoEAFi6dCmys7Nx+PBh9OrVCxs3bkR5eTlOnTqF1NS2yPbXXnsNU6dOxYIFCxAXF4eVK1eisbERH374IYxGIzIzM3HkyBH8/e9/x6xZs2Aw6ONXIkF4ip5971KwflH+tn8XfLCjUnHmzpcHq/A/ub3d5sJffe6UPBgcH/xaxOEouebczBTk3NQZHxdX4kTNJXRLjEZ+dhoiwpwdCGpir8RcJ1omUeyuqEFNQ7PsuGCm7lILdhy7wDTWcd3urqhB3WW2Yol6jM0TQ3WLjebmZqxYsQIPPvigoKg4d+4c1q9fj2nTpinab3FxMUwmk10EAcDgwYNhMpmwc+dO+5jMzEy7CAKAO++8E01NTdi7d699zLBhw2A0Gp3GnD17FpWVlaLHb2pqQn19vdM/gtArhWVVGPryZkxaWiLrftAbrF+UIzLMeOf+gTCblH2xCpnneVHh+kuYFxXenDelDwbeTeVpSxql11xYVoVhr2zB/PWH8M/iE5i//hCGvbLFbZxcOw0xxFwnvDXK9T6bTZGKUqwDyRLhT87UXmIad76+0b6+WOc2Pkq+8KaeUB0s/cUXX6Curg5Tp04V3P7RRx8hNjYWEydOVLRfi8WC5ORkt9eTk5NhsVjsYzp37uy0PSEhAREREU5j0tLSnMbw77FYLEhPTxc8/sKFC/HCCy8oOmeC8AeB3rxWiTskNMTgFFx/9NxFLNlyXPYYrjE2SlO0tYS1CKCjpab4eLVHLWmUXrPYmqqyNuLRFfvw5IgbMSOnB0JDDJJWHBaEHqpaJFEEkiXCn7B6RRZs+BEf7KjE8+MymOf2D0PSAsIqzaPaIrRs2TKMHj3aySrjyAcffIApU6YgMlL5ohS6QRzHOb2uZgwfKC21AObMmQOr1Wr/d+rUKcXnTxDepj00r1UanOsYXD+kRyew4PjFrSRF2xuwNKp8ckRPbJ+dYxewnrakUXLNLMHci4uOYMii7+zWITErDgtiD1VPkyjUWqqCjcaWVvlBV+F/XNU2NMnObUJ0OGbk9PT8BH2IKiF04sQJFBUV4aGHHhLcvm3bNhw+fFh0uxRmsxnnzp1ze/3nn3+2W3TMZrPd8sNTW1uLlpYWyTHnz58HADdrkiNGoxFxcXFO/whCb/j7oa4Vat0hLF3OXRtG+qPPnWum10gRN1+KKRLv3j8QM0fc6PTg9zSbSsk1swZzW+qbnFxquZkp2D47B6sLBuON+/pj5UODYI4zSj4sQwxArZfieDzpjG5A24PcFNn+K8sU/uD+nBWDu/rvuXVlyM00262JrhjQ1lQ3kKxBgErX2PLly5GcnIy8vDzB7cuWLUNWVhb69euneN/Z2dmwWq3YvXs3br31VgDArl27YLVacdttt9nHLFiwAFVVVUhJafui3LhxI4xGI7Kysuxjnn32WTQ3NyMiIsI+JjU11c1lRhCBRntqXqvGHaImuNbXfe6kApS3z85hul5Ps6mUXLPSteLoUnOtETNv/M2y1bMfW7UPb2MgxvRld9+yJgaIZb/FR4djaI8krP++TcQJrZuFE/sg1hiOKct2MZ9XsFDT0ILlOyoFt5njjJg3/mZdu+PFUCyEbDYbli9fjgceeABhYe5vr6+vx6efforXXntN8P0WiwUWiwXHjrVV5ywtLUVsbCy6du2KxMRE9O7dG7m5uSgoKMB7770HoC19fuzYsejVqxcAYNSoUcjIyEB+fj5eeeUV1NTU4Omnn0ZBQYHdgjN58mS88MILmDp1Kp599lkcPXoUL730Ev76179SxhgR8LS35rVqiq0prVCuZYq2HFrFb3maTaXkmpVYD+Vik3IzU/DW5AGYsXo/pLyzM1bvwxKw9bVSmu0nJbDH9hVosuqwr1YbhxRTpMdVzdsz7rc1cJ+ripuubty4EXfeeScOHz6MG2+80W37+++/jyeeeAJVVVUwmUxu2+fNmycYjLx8+XJ74HVNTQ0ef/xxfPnllwCA8ePHY8mSJYiPj7ePP3nyJB577DFs3rwZUVFRmDx5Ml599VWnLLHS0lJMnz4du3fvRkJCAh599FHFQoiarhJ6hJrXXkNJ+QCxRp1aNoDk743YQ1TNvfEk5Z/1mn9pvILMed8ynQ/PG/f1x4T+XQS3sTZxBYB3ZeZdaUNWMRzXSlKMETAA5y82oeaXJiTGRMBsirKvH7E2EoQwvmiiqhTW5zd1n5eBhBChV3zxUG+PeLs4oSdd3NUeUw6Wa577RSk+LjnJfD5y17DuwBnMXHOAaT/x0eHY+9xIwevRSlgKzUF8dDgAOFVKdpyXN4qOYHHRUaZrIPT3A4y6zxNEO4ea16rD0xRtOVHhjfgtT4tmslxzZTVbXRmAzY2oxC1bd6kFSzYfw8wR7tlGahuyOiJmURJqFeHovpyR0xOrd5+CpZ5cZCz4o0eeFpAQIogAhprXqkNtA0iW2B+t47e0qoQtd81dE6KY9wXIV3rm45NY42yW76yw1yhyxFNhqbTHm2t9pXnjMwQtr4Q4gZCk4YjqOkIEQegDal7rG1hrN2V1S1Cc2i+GLythX5fIJoSiw9ncro5p7CzUXWoRDNj2VFiq6fHmaNnIzUzBw3ekIxBzbPx1yoGSpMFDQoggVOLtbuCE75G6p6wumr0nahUVipQ6F18WzayyNjGNuzurG7MlKjczBW9PHsj8QBayJKipGeUIayd6sfMpLKvC+1srJLPf9AoHIMYY6tNjJsaEI6tbgk+P6SnkGiMIFfircSfhPbSM/ZnQv4vH8VtaxMYooVtiNNO4tI5s43jG9E3BzHM98Pp3x2THClkSPCkh4Gkn+vP1TXhry7GAdok1NLFXkNaCmoYWDHtlS0B9F5IQIgiFBHqPL8Idb8T+eBq/pSY2RiqoWi7gOj87DQs2HJK0fIQY2saxwh+za2IMIsND0NhiExwnF3ytNjHAk070IQZgwYZDqt4bDMRHhwsGmwOB911IQoggFODvxp2E9rDe0//+ebjigoxqg7IB5bExUhYtXL0GKQtmRFgICm5Px3tbK0SPVXB7OiLC2CIqhM5HCFZ3oRph6UnQbiC6wnyFAYAx1IDEmAhBoRlo34UUI0QQCmgvPb6Ia/g69ocVJbExUkHVj67Yh0cZA67njMnAyIxkweONzEjGnDFswc8bvq8SPKYQcn3lHFGaGKAmaFffj2x9wAE4d7FZ0toWSN+FJIQIQgHtqccX0YaSe6q2SawrLIH2Us1DHYUXANmgaiGEAq4Ly6pQVH5ecHxR+XmmLLUN35/FjNVsFZk7xkTgv38e7jX3iZpO9GQI0pZA+C4k1xhBKKC99fgKZDwtMsjj69gfJYH2LLExxcerVffEcvzVfmt6omy9HTlXR2FZFR5btZ/5+NUNzdh7otZrxfccA63lMACIjghFQ7Nvg4vbO4HwXUhCiCAU4MvGnYQ4Wmbtqbmn3izIKCSGpISXFr+4z19s9DhLjY+1UnNsx314ozioSSKwF4A9G41EkHYE0nchCSGCUICn3cADFW89oNSgddaer+6pJ4H2UsJLi1/cybGRHrt91RQu5I8NeKckhdhaccVsisTlllZJsUQoJ1C+CylGiCAUolWcSKBQWFaFoS9vxqSlJZi55gAmLS3B0Jc3a1rVmBVvFRn0xT31VqC9mjgYHseAa0/dvmosUyzB3moraCtprfH7W64nEcQIa4XtJ0bcGDDfhWQRIggVBEuPL73VTPJmkUFv31NvBdrLWbQ4gf/n/wau/Wq/NT1RsjYM0FY7RszVodQyZQBbsLfaNGwlFqoPd1Yy7zcY6WAMwz1ZXbB85wlwjL8x0pKUFd70J2QRIgiVtPceX75u8cCCt7P2vHlPvRloL2XRevf+gXh78kAkxES4bVMqZKVmQ4llKsXh2N6ylClZA3WXyRokxS9NV7DuoDKrXCAESfOQRYggCEG0tL74K8NLazy5Dm8H2otZtDaVWzB/fblTzZfEmHDMzevtJIJ2V9TIuodqrzZGFbrfUpYpnj/c1g2jbk5RFeytVNwqXQMxxlCft6MIJFgrdAdSkDQPCSGCCFC8HcCs1QPK3xleWuHpdfgiKNs1qFrMtVnb0ILpq/bjnRCD/dwt1stMx5AaJ5buLzVP3hK3t6YnIjEmHDUNbNYeEkHaEShB0jwkhPyEnrJwiMDDF01fWR88Fy42odXGCa7fQM3wckWr61DbM0sNSrPUWH/xy41TGmvlLXEbGmLA3yZkKqprRHhOIAVJ85AQ8gPUuZzwBF8FMMs9oHjmrz+Ef2yvcFu/3urL5ksxAWh/Hb4KtGd1bX64owJTh6QjPjpCdKwjLOOU1Fnyprgd0zcVj5yuk+yfRmiLVJC0Xg0AJIR8jN6ycIjAwpdNX1liPniE1m8gZ3g54o3r8KQZKyusrk1eyA5hPJ+6S+q6uUvhTXE7Z0wG+l2XgFmfHkBji02L0yUkELMkF5ZVYd6XP8BS32R/zRxnxLzxN/v9mUdCyIdQ53LCU7wpLoQQe0AJHdd1/foqw8vbBGp/OSUxNRZrI/697wzT2MQORrWnJIk3xe2YviloarXhyU8OeH6ihCBSLszCsrYmvK5Y6pvw6Ip9eNfPBgBKn/ch1Lmc8BR/PJRzM1OwfXYO5ub1lhznun79neGlFYF6HUrS2ZUUQDDHee86vVm+wJvnHexIuTBbbRyeWVsq+f5n1pb6tAyHKySEfEig/rIk9IO/HsqhIQYkxbJZAvj1K/cgdqxqrGcC9Tp416aWjxc9XicrnlTgJqSRqklVcrxatixD3aUWlByv9tbpyUJCyIcE6i9LQj/486GsdP3yD2L+vBwJpL5sgXwduZkpeHJET8XvE7pOvhK0Hq+TBan7SKhnbl5vbJ+dI+raKv7pAtN+WMd5AxJCPiRQf1l6SquNQ/Hxaqw7cAbFx6v9agINdPz5UFazfttLX7ZAvo60pBhF458ccSM6xwXedbIgdh9dPy7REaGIMYb68Mz8RwdjKHJv7oyE6HBVAjEp1ijzfcO6V//JUwqW9iHB2LmcSgVoj6/Tx3nUrl9PgmD1lG4bqP3llFiYU0yR6JncAa5RQxxrgykR9H4fs7ol4J3/HMfyHRWou9yCS81txRWDodp0Q1Mrvv3hHB6+Ix3vb62QzRB1RW59ZXfviCVbjsnuxxeJD2IYOE9XeDunvr4eJpMJVqsVcXFxmuwzWMSBWKkA/uuvPfzC9Cf+erj4av0KHSc+Khx/GJKOGTk9dC9AfInUWmi1cRj68mbZelAA8MjVh6GWn9lA+L4T+64KFviMr7l5vTF//SGmZrX8e7bPznFaa67rEACy/rZJMk6ogzEM++aORESYtk4q1uc3CSEZvCGEAH39QvIG/Jev2AdK6ENEBA7eXr9yD6b46HAsmthHNw9Sf8IiNPj5BIR/7cdHh+OluzIlH4JqPrOB8GNI7rsqmFhdMBi3pifaP9uVFy5hcdER0fGOae9S6xCAYPq8I94Qx6zPb4oR8hPtvXM5lQpo33hz/UrV2+Kpu9SCR1fsQ2GZso7Y7Q1eaLh+1vgCl/z8iMXGxEeF43cDu+D5cTejytqo6WdWrm4a0FZ3yt8xg3LfVcHE+YuNTp/tXuYOTO+TW4dAm2iSKmHgumZ9CcUIEW5o8WufSgUQalHyYArmAqSsQoOfH9fYmMoLDVi9+yT+ve8MczFFgP0z6+vin2I0X7Hh4+JKnKi5hG6J0cjPTnNywRSVW7x27ECj8kKD/f/59SUGX0A156bOTIWCt8/OQc5NnTF4YZFgI1x/FhUmIUQ4oZU/n0oFEGpRIo598SDVKyyCscraiCWbj2LmiBsBXLPkFZZV4fWio6piYlg/s3r4MbRwQzmWbquAo9FpwYZDKLg9HXPGZKDVxuHzA+wisL2zuOgoepljkZuZwixkPy6uVGRJFBJBQmN9+Zkm1xhhh9XMzkKwlgogPEepONb6QRoo5R5Yr3tx0VGnzy6L61GK/tfHM43z94+hhRvK8d5WZxEEADYOeG9rBRZuKMfuihrJB3MwMudqlWfW9XWi5hLTuPMXG3UhjoUgIUQA0N6fH8hF6Aj/wotoVrR8kBaWVWHoy5sxaWkJZq45gElLSzD05c26jEVSct2On11PY2JW7TrBNI71x1BWtwTNhWfzFRuWbpPuOL90WwXO1l32+FjtjdpLLViy+Sjz+uqWKN5t3pHk2Ei/i2MxSAgRALwT3BzIRegI/8GLaDmJ7KlV0dXys+F77SyiWiJmoVIiGB0/u57+2ma1ALD8GBrfLwXDXtmiufD8uLjSzRLkio0DDpyq9eg47ZXlOyqR1S2BScjmZ6cxW//16imgGCECgPf8+YFahI7wL7yIfmZtqWD9EU+tikKxcCEG4dRyfwZxysXsPT8uQzYtmeebq+IiycPu8awWAEC6+Of4fimCNYt44fnmfQNw7mKjaJCzFKxiDWibT8oac6bucgs+Lq7E3LwMTF8lXUA1IixEUaFVPRYVpjpCMnirjpDeKD5ejUlLS2THrS4YHJSBqYR/aLVxWLL5KJbvqETd5WuCyJOaI54Uz/Pl+metwfNG0VHJWi+umOOMaLxik22EKcahF3MRFaGs/USrjUPJ8eqr/aQMGJSeiD//+yAs9U3M+zAAyLmpEx66vbvsj6ll237C/PWHZPd5R88khIca8N2PPzOfRzCRclWwfnmwSjaBRkmija+KbFJBRY0IFiEkV32WCiAS/kSrAo6eFs97477+mNC/i6r3KkFJQVIAGLLoO2ZRobSFgitqxKDQg88T5B6azVds6PXcN0FbKVor+E/YW5MHICHGKPv5U/I59UVRYSqoSCiCgpsJPaNVAUdPA4V9FcSpJGYvNMSAeeNvtneIl8NTcaDUPS6WjeoJcnFbEWEhGNvXrNnxghV+rcxffwi3pifKfv6UfE71VFSYhBBhh4KbifaO2kBhXwdxKo3ZE/vsegMlYtDTVH0xxDJZHQPLu3Vkq4pMSBMMXQAoWJpwgoKbifaMGouOPyyiatKMHT+735RV4Z/FbGnurPDuOCVi0JvtK1yL72ntfiOcac9dAEgIEW7wJkuCaG/w6btSndhDDHBKvTb7sFM6HzdhqW9EYky4aLE/MVHi+NnVWghxUC4GffHwLCq3wHq5Oai7x/uCpBjPsg31DAkhgghSfBGsqDf4WDip9N0lk9gCQ7WG1aLBYqGSE3wGtHWcr73Uwhw8HR8dzjDKGV/EVC3bUYnP9p8hEeRlnvr0IOaNZ/9BEEjfL5Q1JkOwZI0RwYWv0lf1it6uX0lKP+t58vsEhAXfO/cPBABF7iQDgCdG3Ii0pGimh5tcNqoQI3p3QtEhSmfXG65lG6TQy+eL0uc1goQQ0d5grU/T3tHLL1aWVPnEmAg8l9cbZlOUovNkeSDxNX6mr9rnVKuJBbmHG18HanHRUab98S6/sX1TsGy7e58wLehgDEND0xUAnmfQBSMpMmVU9PT9wvr8JtcYQQQRcj3l/FVB2R/oJRaOJVW+uqEZZlOU4vNlSX4IDTEgJMSgWAQB19LYhR5uaoKX+QDonJs648933oSPiyvx732ncajqouJzE+OXpit4csSNWLPnpNO5eVpfKViosjbize+O4ImRvZxe5wX1M5+VBtz3CwkhgggilNSn0YNICAa83ZFbTvC12jjsOHZB1b7FHm6eVO8G2q41IiwE026/ARmpJqaq90pIS4rG9tk52F1Rg6JyCz4/cMYpMD0yLASNV2yaHrM98fp3x3C5pRVzxrTVnmMVvXr9fiEhRBBBhLcfuoRy/NmRW4uUc9eHmxa1gxyvVUngt5L9h4YYYL3cjA92VLrtl0SQPO9trUC/6xIQEgLFoldv3y9UUJEgggh/PnQJYfzVkVvris/8w82T2kFC18pS9X7hxD549/6BMEXJ/7bn9++pYOtgJDvCc+vKMO/LHxTPod6+X0gIEUQQ4a+HbiDjWK24+Hi1UyVjLfBHextvVHzmH26eVO8GhK+Vper9yAwzosLlm8HOzWvbv1rBxrcyKbj9BsXvbW/UNDQrapwL6PP7hSQtQQQRLHV0qKfcNYRcR4kxEfjbhEyM6atd5gv/oHc9lreKOWpd8dnx4cb6a9+1YKTctcoFfrcVopR/KCfERABQL9gSYyIwoX8qWlrJfaYGPX6/kBAiiCDD1w/dQEUs4LemoRmPrdqHR06n24NFtcCX7W20jtHgH26tNg42G4f4qHDRLDQ+Rf6/fx6OvSdqFV2rVOC30vg3Je6ZURmdERYCFP9UjeqGtrgiQjmjM80YmaG/ZrgkhAgiCPHmQ1cv9Xk8gcV11BYsGo8xfVM1O66vUvq1itGIjw7Hool9kJuZwhR47Wh1jAgL0fRalca/3ZqeKNnGxJGN5ec8Orf2isEAKKlE+E2ZBUNf3qy7H1wkhAgiSPHGQ1cvFWU9hdV19Ny6MtyZmRJwQo+l55oU8dHh+MNt6ZiR0wOhIQbmdHlPrY6OvdhqfmlCYkyEvchkbUOz7PsdXXihIQb8bUImHlu1X9W5EMpEEI9U7Sl/QUKIIAhNEHsY6vGLTw5WN0tNQ4vuaqKwIBUrJkV8VDjemjIQg2/oaBd/LNYzofcpRcriZIoKwxWGIPa5eb2djj+mbyoeOV2H97ZWqDonQjl6LKyoKGssLS0NBoPB7d/06dMBQHCbwWDAK6+8Yt9HU1MT/vSnPyEpKQkxMTEYP348Tp8+7XSc2tpa5Ofnw2QywWQyIT8/H3V1dU5jTp48iXHjxiEmJgZJSUl4/PHH0dzs/IugtLQUw4YNQ1RUFLp06YIXX3wR1FGEILRHrmI10PbFp3XGlbdQ4jrSW00UVsQysYTgM6UW3d0HQ3okOT28WKxndZdbEGIweCSCpFL9rZevoKGpVXY/CQId1OeMycDbkwci8WoQNeF9HGtP6QFFFqE9e/agtfXaYisrK8PIkSNxzz33AACqqqqcxn/zzTeYNm0a7r77bvtrTzzxBL766iusWbMGHTt2xFNPPYWxY8di7969CA1tS32cPHkyTp8+jcLCQgDAww8/jPz8fHz11VcAgNbWVuTl5aFTp07Yvn07qqur8cADD4DjOLz55psA2nqMjBw5EsOHD8eePXtw5MgRTJ06FTExMXjqqaeUzhNBEBK0t4rVbfEjEahhcLforSaKEoRixWobmvHi1+Ww1F+7n6aocPxhSLpgoCurELTUN2LH0Qso/ukCgDa3LIuFSMtU/0/2nERWtwREhDnbAMb0TcGdmW3z8E1ZFf5ZfEKDoxFy6OVHhCIh1KlTJ6e/Fy1ahO7du2PYsGEAALPZ+UOybt06DB8+HDfc0FZvwWq1YtmyZfj4448xYsQIAMCKFStw/fXXo6ioCHfeeScOHTqEwsJClJSUYNCgQQCApUuXIjs7G4cPH0avXr2wceNGlJeX49SpU0hNbQtUfO211zB16lQsWLAAcXFxWLlyJRobG/Hhhx/CaDQiMzMTR44cwd///nfMmjULBoP/zXEE0V5obxWrr8WP7JMcp8eaKEpxjRUrLKuCq7Os7nILFhcdwZo9J91ifFiF4Jy136Ox5VrK+ZItx5yCrcXQMtX/iwNn8eXBsyi43T3jz3EelAqhFFMk5uZl4Ll1pUzB10QbevkRobqgYnNzM1asWIEHH3xQUFScO3cO69evx7Rp0+yv7d27Fy0tLRg1apT9tdTUVGRmZmLnzp0AgOLiYphMJrsIAoDBgwfDZDI5jcnMzLSLIAC488470dTUhL1799rHDBs2DEaj0WnM2bNnUVlZKXpdTU1NqK+vd/pHEIQ0rF9olRcuiW7zduFCpYzpm4JH7kgX3W6APmuieALvghKrx1N1Nd6rTSy1IVekk8dRBPHUXWrBoy77c0Vr8Wzj2jL+Fm4od9vmmP6vhPH9UjCmbwpK5owgFxsDeivcqloIffHFF6irq8PUqVMFt3/00UeIjY3FxIkT7a9ZLBZEREQgISHBaWznzp1hsVjsY5KTk932l5yc7DSmc+fOTtsTEhIQEREhOYb/mx8jxMKFC+2xSSaTCddff73oWIIg2rg1PRHmOPf4C1fW7DkpKHAKy6ow9OXNmLS0BDPXHMCkpSUY+vJmyQekL2iLHxmAxBjnB2OKQ0Xj9gKrC4qDc7yXVGVsVoTix3hhfPScdp3nHVm6rQLNDj3F+DU4Zdku0RpIYry/tQKFZVWICAvBS7/NVD0P/kbLtiEJ0W2fGV9VS/cE1UJo2bJlGD16tJNVxpEPPvgAU6ZMQWSk/C9FjuOcrEpCFiYtxvCB0lJusTlz5sBqtdr/nTp1Svb8CSLYCQ0xYNKtXWXHCQVIigXCWgSsD/5gTN9U7PnLSKwuGIw37uuP1QWDsX12TrsSQYAyF5TrfRQLvGaNQHDdn6MwXrLlONtOFGLjgPlf/4B1B85g8aYjeNTDvmu8mOPnIoUhCF1PxEeH46Gh4hZQpfD936TaougFVfLvxIkTKCoqwtq1awW3b9u2DYcPH8Ynn3zi9LrZbEZzczNqa2udrELnz5/HbbfdZh9z7px78aqff/7ZbtExm83YtWuX0/ba2lq0tLQ4jXG1/Jw/fx4A3CxFjhiNRid3GkEQbKQlxTCNc3R1yGWb6SXN1leFDv3JmVpxt6UQri4r18DrCxebMH/9IcX7Y61JpAUfl5zExyUnPd4Pnwzw4Y4K5GenwRQVgZvMsZq2MfE2dZda8Pp3R0WLJEZHhCIiLAR1l9isZYctv2BGTg+fVUv3BFUWoeXLlyM5ORl5eXmC25ctW4asrCz069fP6fWsrCyEh4dj06ZN9teqqqpQVlZmF0LZ2dmwWq3YvXu3fcyuXbtgtVqdxpSVlTllqW3cuBFGoxFZWVn2MVu3bnVKqd+4cSNSU1ORlpam5rIJgpBATWd7JdlmhDpYY6++/UE8ZECIygsNbq/xgnFC/y5IilX2gzI5NtIrzWB9yfz1h9Br7jeYtLQEWw7/7O/TUYVYhZkpg7pi97MjMDevN/IHd0VspLQdZXHREQxZ9B02lVvsayK7u/o6Ut5EsUXIZrNh+fLleOCBBxAW5v72+vp6fPrpp3jttdfctplMJkybNg1PPfUUOnbsiMTERDz99NPo06ePPYusd+/eyM3NRUFBAd577z0AbenzY8eORa9evQAAo0aNQkZGBvLz8/HKK6+gpqYGTz/9NAoKChAXFwegLQX/hRdewNSpU/Hss8/i6NGjeOmll/DXv/6VMsYIwgvIVSvme0w5Bki2t2wzvaGk0vcphRahxUVH0cscK+riUJIRlBgTgaxuCczuuenDu+OctQmbyi2wNl5hPo4vaK+l6pZuq8Cne08zW4QAwFLfFBDFVBVbhIqKinDy5Ek8+OCDgtvXrFkDjuMwadIkwe2LFy/GXXfdhXvvvRdDhgxBdHQ0vvrqK3sNIQBYuXIl+vTpg1GjRmHUqFHo27cvPv74Y/v20NBQrF+/HpGRkRgyZAjuvfde3HXXXXj11VftY0wmEzZt2oTTp0/jlltuwWOPPYZZs2Zh1qxZSi+ZIAgGpIJmxQIk1ViRCDaUxF612jicqFYmhABgztpSUQsTL4xZqGloxrBXtqConM0qdWPnWLx6bz/s++sop9ittycPCLjYnEBCiQhyRO/FVA0clVqWpL6+HiaTCVar1W5tIghCHCVWiFYbh6Evb5a1Im2fnaNLk7pe4edVzLriOq/Fx6sxaWmJqmOtfGgQhvRIEtymJN5HSauP1QWDRWO2+H5kReUWLKMu8bphxvAeGNIjyacxQqzPb+o1RhCEpijpbC/V80qPabaBgtJK3564HouPV4sKIT6DSq4rPX9OABByNViX1b3qSmiIAbemJ2LWvw4wnT/hG5ZsOYYlW47psgmz6vR5giAIMRyDZuUCJMVSr/WYZhsoMLe9sF6+WqvnFw+OJm3Hyc1MwfbZOVhdMBijM91bdLhiExFB/JFGX22FIeVq0bIatRC9ktkyJAl39FIWwxGyCBEE4XeUWJEIeVhjquavP8TUT02K7BuErUGOhIYYYL3cjG/KlGWmCfHBjkp8sKNS0rLg7eD6yprLXt1/e0ZPZTF4yCJEEIQuUGJFIqRhbXvhqQiKMYbiVwxtEvi0eC0RavfB4+3g+qYrNsRE0ONTLXori0F3kiAIop2hRdsLFhqaWjHslS2ybg5vuapc233wsApBT7jjRvdWUIQy9FIWg4QQQRCEzlHTkFYs9sq1b5qnsMR8bGJMi1eDkGVBrpSDAcATv+nh0XG7d4ppayERFxzp+gOuj2ca16NTB9zVX7j1lit6KYtBMUIEQQQcfIq0L+OJ/HFMQFk5AleEYq8s9Y148pMDmp2fXMxHYVkVPvByGvvZustYtu0nnKi5hG6J0cjPThPNWDNfnbuRGWZ88n+nRUs3yJF9QxKG9EyCzQY8t67MYzej3tl/qo5p3LGff8Gxn39BiKEt8F0Iluw/X0J1hGSgOkIE4RtYhYYnwkAtWh5TiaASq8PDj1aTVedJzSA5XOv7yNUzcsQcZwRgwLl65cLEtT9WiAEouD0dc8ZkSM43P78Aew0joK1B6d7nRmJTucVnfdHaC56sXaVQHSGCIAIGVqEhJgx494w3vly1PKbSYpPeaEgr1wrFE1xjPpTEBs0bfzMA2IWJElx/zts44L2tFQCAOWMyRIsvjsww44kRN2L5jgrUXb5WNTk+OlyyivJLd/VByU/VmPWvgySCFGKKDseiiX10VRaDYoQIgvArrK0g5IQBoH0pfy2PqaTlBeC9hrTeDKR2jflgDYadNqTNlZWbmYK3Jg+EVh7Hpdsq0HzFJritsKwKQ1/ejMVFR+wiKMYYiseHd8dbkwbiwSFpiIkIdXtfTEQonv2iFFP+sQuXmlu1OdEgwqqyTYc3ISFEEITfUCI0/NGpXqtjqhFU3mxIKxZIrRYD2ixbrjEfrMGwIzKuFVpMiIkQjS1Rio0DPi6udHtdTJQ2NLXif7ccx5Rlu/DBjko0CAidhuZW1T23iLb1/szaUuw4ekE3/cfINUYQhN9QIjT80aleq2MqbXkBeL8hrWsg9ZYfz+OLA2cV70eqFYqcG04oaNZi1bZYYUV1A4qPV9tjhLK6JYiK0mAhKsyAy1f8NwN1l1owZdku3bTbICFEEITfUCI0/NGpXqtjqhFUakSEUvgilgCQ1MHIJIQSosNR62ARMUs8zNT0ktM6+2rdgbNYUXLS/ndiTES7z/CSw58iyBFvxvYpgYQQoRh/pRET7Q8lQsMXwsAVrY6pRlB52pBW6ed08A0dZYOEE6LDsevZEdh7opZ5v3Jp7K4PwMQORtF9qeFi4xWnv4NdBOkJvbTbICFEKMIfqctE+0WJ0GARBnPzMjQV6Z6KER61gkqpiOAR+pwmxkTgbxMyMaav8HtCQwxYNLEPHpXI2lo4sQ8iwkJEs7DEUNJLLlgKFOodU1QYRvTujE3l51DvIia1RMgt7GuojpAMVEfoGt6oaUIQYrVcxNaVmBgf3y8FXx6s8opI1+IHgNLrdESL2kM8j9zRVl9HiIUbyu1p50rfqxVKag+J4SpaCeXIWQe15o37+mNC/y6a7pP1+U1CSAYSQm3IfTnxv2i3z84hNxmhGKVCw1UY1DY0Yfqq/V4V6Vq4hL1tUWUVEW9PHoAxfZ3bIDRfseGmud9IZmyFGIAf549GRJh3E46lRKPcAys2MhTPj70ZT//7e2+dHsGIEkHqWoxTC6igIqEparJeCIIVJa4TwDnIl3/4a114UOqYalF6nUphLWD43Loy3JmZ4nTcj4srZdPW+XT0abff4OmpSiLlEhzfLwXvX7VaCVnWXvldP5iiIrx6fgQbHIC7B3bBd4fOOxWsdEQP7TZICBFM+CN1mQgu1AqNQBPpWggqMVg/fzUNLW7zcaLmEtN7Wcd5ipRoHNA1QTJuqtXGea16NqGM7348j5d+m4npq/YDUB9n501ICBFM+CN1mSBYIJF+DSWfP9f56JYYzfQ+1nFaICYa5SxrjkHuhH+pu9SChBijqqB/X0FCiGDCH6nLBMECifRr3JqeyFwnx3U+8rPTsGDDIdkYofzsNA/PUhvkLGu8e+2Zz0pF3TKEZ7DGAJ2/2IgJ/bt41S3sCdRig2BCqj+RXsybRHDCi3SxlSfW/qE9EhpiwN8mZMqOE5qPiLAQFNyeLvm+gtvTvR4orSV87zKlmOOMMGj0VdbevhETY8Kx+Pf9sbpgMGb+pgfTe3jRzYvXCf27ILt7R908LwJnRRN+R6w/kdkUSanzhN8gke7MmL4peOQOcUFjgPh8zBmTgUfuSHdrehpi8E3qvDcY3L2jrFA2xxmx8qFBeOO+tgf8jmd+g4dlRKEUvxt4nX1fM3/TU/V+9ITh6r+XftsHvx3QJmT+9JsbER8dLvmeQPgRQunzMlD6vDtUWZrQI1Ts05kN35/Fc+vKUNNwzS3EOh/NV2z4uLgSJ2ouoVtiNPKz0wLKEuSK2hpOCzeU4/1tFVDylEyIDsf/PTfS/p3YauOQ9bdNPm/U2sEYhl+atCuEKLZ29FxfjuoIaQQJIYIIHEikO0PzcQ21Qrn5ig3Pri3FhtIqXGpx70bviAHCD/7CsirJit2O5zM3LwNHz1/Ee1t/wqVm6eOJ7YO3kAqJPzGMYSFoumKz/22OM2LSrV2RlhTDVMRTjz9CSAhpBAkhgiCI9oEnwtDxvZUXLmH17pOw1LM/+IXEgpTYaLVxWLL5GN7belxUEI3ra0bOTZ1R09CMxA5GmOOc9yF0zBADnALiO8ZEYP6ETNyZ6Vkgsx5FNwkhjSAhRBAEQbii5sGv9j0lP1Vjx9ELOHi6DtERobg1vSMeuI3NXel6zKxuCYqa5gYyJIQ0goQQQRAEQQQerM/vwI1+IwiCIAiC8BASQgRBEARBBC0khAiCIAiCCFpICBEEQRAEEbSQECIIgiAIImghIUQQBEEQRNBCQoggCIIgiKCFhBBBEARBEEELCSGCIAiCIIKWMH+fgN7hC2/X19f7+UwIgiAIgmCFf27LNdAgISTDxYsXAQDXX3+9n8+EIAiCIAilXLx4ESaTSXQ79RqTwWaz4ezZs4iNjYXB4Hljuvr6elx//fU4deoU9S4TgOZHGpofaWh+5KE5kobmR5pAmh+O43Dx4kWkpqYiJEQ8EogsQjKEhITguuuu03y/cXFxul9E/oTmRxqaH2lofuShOZKG5keaQJkfKUsQDwVLEwRBEAQRtJAQIgiCIAgiaCEh5GOMRiOef/55GI1Gf5+KLqH5kYbmRxqaH3lojqSh+ZGmPc4PBUsTBEEQBBG0kEWIIAiCIIighYQQQRAEQRBBCwkhgiAIgiCCFhJCBEEQBEEELSSEJFi4cCEMBgOeeOIJ+2tTp06FwWBw+jd48GCn9zU1NeFPf/oTkpKSEBMTg/Hjx+P06dNOY2pra5Gfnw+TyQSTyYT8/HzU1dU5jTl58iTGjRuHmJgYJCUl4fHHH0dzc7PTmNLSUgwbNgxRUVHo0qULXnzxRdm+Kp4wb948t+s3m8327RzHYd68eUhNTUVUVBR+/etf44cffnDaRzDPT7CvHwA4c+YM7r//fnTs2BHR0dHo378/9u7da98e7GtIbn6CeQ2lpaW5XbvBYMD06dMB0NqRm59gXjuScIQgu3fv5tLS0ri+fftyM2fOtL/+wAMPcLm5uVxVVZX9X3V1tdN7H330Ua5Lly7cpk2buH379nHDhw/n+vXrx125csU+Jjc3l8vMzOR27tzJ7dy5k8vMzOTGjh1r337lyhUuMzOTGz58OLdv3z5u06ZNXGpqKjdjxgz7GKvVynXu3Jm77777uNLSUu6zzz7jYmNjuVdffdVr8/L8889zN998s9P1nz9/3r590aJFXGxsLPfZZ59xpaWl3O9//3suJSWFq6+vp/nhaP3U1NRw3bp146ZOncrt2rWLq6io4IqKirhjx47ZxwTzGmKZn2BeQ+fPn3e67k2bNnEAuC1btnAcF9xrh2V+gnntSEFCSICLFy9yPXv25DZt2sQNGzbMTQhNmDBB9L11dXVceHg4t2bNGvtrZ86c4UJCQrjCwkKO4ziuvLycA8CVlJTYxxQXF3MAuB9//JHjOI7bsGEDFxISwp05c8Y+ZvXq1ZzRaOSsVivHcRz39ttvcyaTiWtsbLSPWbhwIZeamsrZbDaP5kCM559/nuvXr5/gNpvNxpnNZm7RokX21xobGzmTycS9++67HMcF9/xwHK2f2bNnc0OHDhXdHuxrSG5+OI7WkCMzZ87kunfvztlstqBfO0I4zg/H0doRg1xjAkyfPh15eXkYMWKE4Pb//Oc/SE5Oxo033oiCggKcP3/evm3v3r1oaWnBqFGj7K+lpqYiMzMTO3fuBAAUFxfDZDJh0KBB9jGDBw+GyWRyGpOZmYnU1FT7mDvvvBNNTU12M3lxcTGGDRvmVNjqzjvvxNmzZ1FZWen5RIhw9OhRpKamIj09Hffddx9++uknAEBFRQUsFovTtRuNRgwbNsx+XcE8PzzBvH6+/PJL3HLLLbjnnnuQnJyMAQMGYOnSpfbtwb6G5OaHJ5jXEE9zczNWrFiBBx98EAaDIejXjiuu88NDa8cdEkIurFmzBvv27cPChQsFt48ePRorV67E5s2b8dprr2HPnj3IyclBU1MTAMBisSAiIgIJCQlO7+vcuTMsFot9THJystu+k5OTncZ07tzZaXtCQgIiIiIkx/B/82O0ZtCgQfjnP/+Jb7/9FkuXLoXFYsFtt92G6upq+zGFzsnxnIN1fgBaPz/99BPeeecd9OzZE99++y0effRRPP744/jnP//pdNxgXUNy8wPQGuL54osvUFdXh6lTpzodL1jXjiuu8wPQ2hGDus87cOrUKcycORMbN25EZGSk4Jjf//739v/PzMzELbfcgm7dumH9+vWYOHGi6L45jnNS5Y7/r+UY7mqgmdB7tWD06NH2/+/Tpw+ys7PRvXt3fPTRR/agO6FzkjufYJifWbNmBf36sdlsuOWWW/DSSy8BAAYMGIAffvgB77zzDv7f//t/kucVDGuIZX6CfQ3xLFu2DKNHj3ayOoidTzCsHVeE5ofWjjBkEXJg7969OH/+PLKyshAWFoawsDD897//xf/+7/8iLCwMra2tbu9JSUlBt27dcPToUQCA2WxGc3MzamtrncadP3/ernbNZjPOnTvntq+ff/7ZaYyrKq6trUVLS4vkGN7M6aq0vUVMTAz69OmDo0eP2rOjhM7J8ZyDdX6ECLb1k5KSgoyMDKfXevfujZMnT9rPCQjeNSQ3P2LvCaY1BAAnTpxAUVERHnroIftrwb52HBGaHyGCce0IQULIgd/85jcoLS3FgQMH7P9uueUWTJkyBQcOHEBoaKjbe6qrq3Hq1CmkpKQAALKyshAeHo5NmzbZx1RVVaGsrAy33XYbACA7OxtWqxW7d++2j9m1axesVqvTmLKyMlRVVdnHbNy4EUajEVlZWfYxW7dudUpJ3LhxI1JTU5GWlqbdxEjQ1NSEQ4cOISUlBenp6TCbzU7X3tzcjP/+97/26wrm+REi2NbPkCFDcPjwYafXjhw5gm7dugFA0K8hufkRItjWEAAsX74cycnJyMvLs78W7GvHEaH5ESIY144gPgjIDmgcs8YuXrzIPfXUU9zOnTu5iooKbsuWLVx2djbXpUsXt/TM6667jisqKuL27dvH5eTkCKYf9u3blysuLuaKi4u5Pn36CKYf/uY3v+H27dvHFRUVcdddd51T+mFdXR3XuXNnbtKkSVxpaSm3du1aLi4uzqvph0899RT3n//8h/vpp5+4kpISbuzYsVxsbCxXWVnJcVxb+qrJZOLWrl3LlZaWcpMmTRJMXw3G+aH101aWIiwsjFuwYAF39OhRbuXKlVx0dDS3YsUK+5hgXkNy80NriONaW1u5rl27crNnz3bbFsxrh0dsfmjtiENCSAZHIXTp0iVu1KhRXKdOnbjw8HCua9eu3AMPPMCdPHnS6T2XL1/mZsyYwSUmJnJRUVHc2LFj3cZUV1dzU6ZM4WJjY7nY2FhuypQpXG1trdOYEydOcHl5eVxUVBSXmJjIzZgxwynVkOM47vvvv+duv/12zmg0cmazmZs3b55XUw/5uhzh4eFcamoqN3HiRO6HH36wb7fZbNzzzz/Pmc1mzmg0cnfccQdXWlrqtI9gnR9aP2189dVXXGZmJmc0GrmbbrqJe//99522B/sakpofWkMc9+2333IAuMOHD7ttC/a1w3Hi80NrRxwDx/mjjCNBEARBEIT/oRghgiAIgiCCFhJCBEEQBEEELSSECIIgCIIIWkgIEQRBEAQRtJAQIgiCIAgiaCEhRBAEQRBE0EJCiCAIgiCIoIWEEEEQBEEQQQsJIYIgCIIgghYSQgRBEARBBC0khAiCIAiCCFpICBEEQRAEEbT8/xd2BQte2CSDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.94503 0.08605\n",
      "amped unit avg and sd usage are 1.01766 0.07695\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f8d72684-2b88-43f7-8509-b77bbfec8db7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 504 time is now 2 sec\n",
      "working on HD 1026 time is now 4 sec\n",
      "working on HD 1534 time is now 6 sec\n",
      "out of 1994 total HDs, there were 1994 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 8 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "6b4cba34-3451-4ffd-a2d9-31a8565a9e72",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 182 overPopped HDs to within 2597\n",
      "working on squaring down HD 962 time is now 0\n",
      "can't drop any more units to HD 962 without creating an enclave\n",
      "can't drop any more units to HD 1321 without creating an enclave\n",
      "working on squaring down HD 1366 time is now 7\n",
      "working on squaring down HD 1409 time is now 7\n",
      "working on squaring down HD 1562 time is now 10\n",
      "working on squaring down HD 1836 time is now 19\n",
      "working on squaring down HD 1943 time is now 20\n",
      "can't drop any more units to HD 1999 without creating an enclave\n",
      "working on squaring down HD 2038 time is now 22\n",
      "We have addressed overpop in a total of 182 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%30 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 0.94503 0.08605\n",
      "amped unit avg and sd usage are 1.01766 0.07695\n",
      "shed unit avg and sd usage are 0.99947 0.07737\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2042\n",
      "working on HD 302 out of 2042\n",
      "working on HD 604 out of 2042\n",
      "working on HD 925 out of 2042\n",
      "working on HD 1229 out of 2042\n",
      "working on HD 1534 out of 2042\n",
      "working on HD 1840 out of 2042\n",
      "all done checking HD and complement contiguity for all 2042 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 1519\n",
      "overpop contigFail, enclaveFail = 0 0 out of 182\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 1646 with pop 251617.0 now has use 0.97339\n",
      "CCB unit 1647 with pop 103725.0 now has use 0.98587\n",
      "CCB unit 1648 with pop 373177.0 now has use 0.93228\n",
      "CCB unit 1649 with pop 284018.0 now has use 0.9747\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "af4a9d26-98ab-4ceb-96c1-f2083abcc5d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will remove CCB unit 1647 from HD 962 leaving it with pop 740526.0\n",
      "we will remove CCB unit 1647 from HD 1321 leaving it with pop 715813.0\n",
      "we will remove CCB unit 1648 from HD 1481 leaving it with pop 426197.66490213\n",
      "we will remove CCB unit 1647 from HD 1999 leaving it with pop 738985.0\n"
     ]
    }
   ],
   "source": [
    "#special for MD -- a few got stuck, need to drop an underused CCB\n",
    "CCBunitList = list()\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        CCBunitList.append(u)\n",
    "for t in range(nHDs):\n",
    "    if HDvPop[t] > 1.01 * aDP:\n",
    "        CCBused = list(set(HDunitList[t]).intersection(set(CCBunitList)))\n",
    "        if len(CCBused) > 0:\n",
    "            CCBdist = [unitCP[CCBused[i]].distance(hdCP[t]) for i,u in enumerate(CCBused)]\n",
    "            CCBpop = [unitPop[u] for u in CCBused]\n",
    "            #pickedCCB = CCBused[CCBdist.index(np.max(CCBdist))]\n",
    "            pickedCCB = CCBused[CCBpop.index(np.min(CCBpop))]\n",
    "            newList = HDunitList[t].copy()\n",
    "            newList.remove(pickedCCB)\n",
    "            unbroken, noEnclave, sList, eList = enclaveCheck(newList, unitNbrs,5)\n",
    "            if unbroken and noEnclave:\n",
    "                print(\"we will remove CCB unit\",pickedCCB,\"from HD\",t,\"leaving it with pop\",HDvPop[t]-unitPop[pickedCCB])\n",
    "                HDunitList[t].remove(pickedCCB)\n",
    "            else:\n",
    "                newList = HDunitList[t].copy()\n",
    "                pickedCCB = CCBused[CCBdist.index(np.max(CCBdist))]\n",
    "                newList.remove(pickedCCB)\n",
    "                unbroken, noEnclave, sList, eList = enclaveCheck(newList, unitNbrs,5)                \n",
    "                print(\"we will remove CCB unit\",pickedCCB,\"from HD\",t,\"leaving it with pop\",HDvPop[t]-unitPop[pickedCCB])\n",
    "                HDunitList[t].remove(pickedCCB)\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8ac73030-0184-42ad-a89a-6f73c0ac78ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 4 HDs with pop < 764431 to within 2597\n",
      "working on squaring up HD 962 time is now 0\n",
      "We have addressed underpop in a total of 4 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SPECIAL FOR MD - REDO SQUARING UP UNDERPOPPED of these 4\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs)]\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "86832df4-4a6a-4300-8b15-6a3e68c633a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 0.94503 0.08605\n",
      "amped unit avg and sd usage are 1.01766 0.07695\n",
      "shed unit avg and sd usage are 0.99936 0.07755\n"
     ]
    },
    {
     "data": {
      "image/png": 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M+fj4KCQkpEZN9fMzMzMVERFR4/gREREuNeceJyQkRD4+Pi41F110UY3jVO/r1atXjWMsWbJEixYtOv8vAwAAdCiNGilat26d3nzzTb399tv69NNP9cYbb2jp0qV64403XOrOXcRsjDnvwuZza2qrb44a8/2Fnupqz0MPPaS8vDznIy0trd52AwCAjqFRI0W//vWv9eCDD+rWW2+VJMXGxurbb7/VkiVLdOeddzpPi6s+M61aVlaWc4QmMjJSZWVlysnJcRktysrK0ogRI5w1J06cqHH8kydPurzO7t27Xfbn5OSovLzcpaZ61Ojs40g1R7OqWa1Wl+k2AADQOTRqpKioqKjGBaq8vLxUWVkpSerVq5ciIyO1adMm5/6ysjJt3brVGXgGDx4si8XiUpORkaH9+/c7a4YPH668vDzt2bPHWbN7927l5eW51Ozfv18ZGRnOmqSkJFmtVg0ePNhZs23bNpfTJZOSkuRwOGpMqwEAgE6uMSu577zzThMVFWX+8Y9/mNTUVLN+/XoTHh5uHnjgAWfNk08+aex2u1m/fr3Zt2+fmTx5sunWrZvJz8931syYMcN0797dbN682Xz66afmmmuuMQMHDjRnzpxx1lx77bXmsssuMzt37jQ7d+40sbGxZuLEic79Z86cMTExMWb06NHm008/NZs3bzbdu3c3s2bNctbk5uaarl27msmTJ5t9+/aZ9evXm6CgILN06dIG95mzzwA34ewzAM2oKZ/fjQpF+fn5Zs6cOaZHjx7G19fXXHzxxebhhx82paWlzprKykqzYMECExkZaaxWq/nJT35i9u3b5/I6xcXFZtasWSY0NNTYbDYzceJEc/ToUZeaU6dOmdtuu80EBgaawMBAc9ttt5mcnByXmm+//dZMmDDB2Gw2ExoaambNmuVy+r0xxnz++edm1KhRxmq1msjISLNw4cIGn45vDKEIcBtCEYBm1JTPbw9juMVvffLz82W325WXl6egoKDWbg7QcR1PkVbHSfdslRyDGr8fAM7SlM9v7mAHAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAICkJoSi9PR03X777QoLC5Ofn58GDRqk5ORk535jjBYuXCiHwyGbzaarrrpKBw4ccHmN0tJS3X///QoPD5e/v79uuOEGHTt2zKUmJydH8fHxstvtstvtio+PV25urkvN0aNHdf3118vf31/h4eGaPXu2ysrKXGr27dunuLg42Ww2RUVF6bHHHpMxprHdBgAAHVyjQlFOTo5Gjhwpi8Wif/7znzp48KCeeeYZBQcHO2ueeuopLVu2TC+88IL27t2ryMhIjR07VqdPn3bWJCQkaMOGDUpMTNT27dtVUFCgiRMnqqKiwlkzZcoUpaSkaOPGjdq4caNSUlIUHx/v3F9RUaEJEyaosLBQ27dvV2Jiot555x3NmzfPWZOfn6+xY8fK4XBo7969WrFihZYuXaply5Y15XcFAAA6MtMIv/nNb8yVV15Z5/7KykoTGRlpnnzySee2kpISY7fbzUsvvWSMMSY3N9dYLBaTmJjorElPTzeenp5m48aNxhhjDh48aCSZXbt2OWt27txpJJkvv/zSGGPM+++/bzw9PU16erqzZu3atcZqtZq8vDxjjDErV640drvdlJSUOGuWLFliHA6HqaysbFCf8/LyjCTnawJoIemfGbMgqOqfTdkPAGdpyud3o0aK/v73v2vIkCG66aabFBERocsvv1yvvPKKc39qaqoyMzM1btw45zar1aq4uDjt2LFDkpScnKzy8nKXGofDoZiYGGfNzp07ZbfbNXToUGfNsGHDZLfbXWpiYmLkcDicNePHj1dpaalzOm/nzp2Ki4uT1Wp1qTl+/LiOHDlSax9LS0uVn5/v8gAAAB1fo0LRf//7X61atUp9+vTRBx98oBkzZmj27Nn605/+JEnKzMyUJHXt2tXleV27dnXuy8zMlI+Pj0JCQuqtiYiIqHH8iIgIl5pzjxMSEiIfH596a6p/rq4515IlS5zrmOx2u6Kjo8/zWwEAAB1Bo0JRZWWlfvSjH2nx4sW6/PLLde+992r69OlatWqVS52Hh4fLz8aYGtvOdW5NbfXNUWO+X2RdV3seeugh5eXlOR9paWn1thsAAHQMjQpF3bp1U//+/V22XXrppTp69KgkKTIyUlLNUZisrCznCE1kZKTKysqUk5NTb82JEydqHP/kyZMuNeceJycnR+Xl5fXWZGVlSao5mlXNarUqKCjI5QEAADq+RoWikSNH6quvvnLZdujQIfXs2VOS1KtXL0VGRmrTpk3O/WVlZdq6datGjBghSRo8eLAsFotLTUZGhvbv3++sGT58uPLy8rRnzx5nze7du5WXl+dSs3//fmVkZDhrkpKSZLVaNXjwYGfNtm3bXE7TT0pKksPh0EUXXdSYrgMAgI6uMSu59+zZY7y9vc0TTzxhvv76a/PWW28ZPz8/8+abbzprnnzySWO328369evNvn37zOTJk023bt1Mfn6+s2bGjBmme/fuZvPmzebTTz8111xzjRk4cKA5c+aMs+baa681l112mdm5c6fZuXOniY2NNRMnTnTuP3PmjImJiTGjR482n376qdm8ebPp3r27mTVrlrMmNzfXdO3a1UyePNns27fPrF+/3gQFBZmlS5c2uM+cfQa4CWefAWhGTfn8blQoMsaYd99918TExBir1WouueQSs3r1apf9lZWVZsGCBSYyMtJYrVbzk5/8xOzbt8+lpri42MyaNcuEhoYam81mJk6caI4ePepSc+rUKXPbbbeZwMBAExgYaG677TaTk5PjUvPtt9+aCRMmGJvNZkJDQ82sWbNcTr83xpjPP//cjBo1ylitVhMZGWkWLlzY4NPxjSEUAW5DKALQjJry+e1hDJd3rk9+fr7sdrvy8vJYXwS0pOMp0uo46Z6tkmNQ4/cDwFma8vnt3cJtAtBe5KZJRafq3u8XJgVziQoAHRehCEBVIHrxCqm8qO4ai580cw/BCECHRSgCUDVCVF4kTXpFCu9bc3/2IWn99Ko6QhGADopQBOAH4X1ZrwOg02rUdYoAAAA6KkIRAACACEUAAACSCEUAAACSWGgNAOeVnlusnMKyemtC/H0UFWxzU4sAtARCEQDUIz23WGOe2ari8op662wWL22eF0cwAtoxQhEA1COnsEzF5RV67pZB6h0RUGvN4awCJaxLUU5hGaEIaMcIRQDaJXdPafWOCFBMlL1ZXgtA20QoAtDuMKUFoCUQigC0O0xpAWgJhCIAblXXtJdvdoF6Szp8skA2v+IGBRmmtAA0J0IRALepb9prgEeq3rNKcxJT9F/vAqa9ALgdoQiA29Q37eWbbZc2SPPH9dNdH5Qx7QXA7QhFANyu1mkvj6qQFB1qk1T/WWUA0BK4zQcAAIAIRQAAAJKYPgMAt+EeakDbRigCADfggpNA20coAgA34IKTQNtHKAIAN+KCk0DbxUJrAAAAEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkcfYZADSv3DSp6FSNzb7ZBRrgkSpLQS9JnH0GtEWEIgBoJpaCdGnNaKm8qMa+3pLes0qVf7FJs/ZKwdHubyCAehGKAKCZeJV8VxWIJr0ihfd12Xf4ZIFWrHtPy7WyaiSJUAS0OYQiAGgmad8Vq7ekw8ahEtPLZd/hygIdNlGt0zAADUIoAoALFOLvI5vFS0uTvtLVVmlOYooOmLwadYMtnNsCtGWEIgC4QFHBNm2eF6fibwOkDdLyWwepJDy2Rl1EYbj0dis0EECDEIoANNjhkwUqqWUEpFqIv0+nvZFpVLBNKqq60WvvLgGSo5YzzI5b3dwqAI1BKAJwXlkFpYpQ3dNC1WwWL22eF9dpgxGA9o1QBOC88ovLFSFp/rh+6tL3ilprDmcVKGFdinIKywhFANolQhGABosOtal3FBceBNAxcSoEAACACEUAAACSmD4DgIar475mTtmH3NcWAM2OUAQADVDffc1cC/0kvzD3NApAsyIUAUAD1HdfMxd+YdzXDGinCEUA0BjhfSXHoNZuBYAWwEJrAAAAEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkEYoAAAAkSd6t3QAAaEmHswrq3R/i76OoYJubWgOgLSMUAeiQQvx9ZLN4KWFdinObQ9kK8TjtUufr7alV8YMVEWCV/MKk4Gg3txRAW0EoAtAhRQXbtHlenHIKyyRJloJ09fnLNHmeKa5Z/Pb3/7T4STP3EIyATopQBKDDigq2/TA1djxVOlMsTXpFCu8rSTp8skBzElO0/NZB6u1xXFo/XSo6RSgCOilCEYDOJbyv5BgkSSoxeTpg8rS/spfkKfVWVVAqMXnO8vOtSQLQcRCKAHRaZ687GuCRqves0pzEFB04KxRJks3ipSCbpZVaCcBdCEUAOq2z1x35ZtulDdLyWwepJDzWpS7E30cRRV+1UisBuAuhCECn5lx35BEgSerdJUBy2GsWFrm5YQDc7oIu3rhkyRJ5eHgoISHBuc0Yo4ULF8rhcMhms+mqq67SgQMHXJ5XWlqq+++/X+Hh4fL399cNN9ygY8eOudTk5OQoPj5edrtddrtd8fHxys3Ndak5evSorr/+evn7+ys8PFyzZ89WWVmZS82+ffsUFxcnm82mqKgoPfbYYzLGXEi3AQBAB9TkULR3716tXr1al112mcv2p556SsuWLdMLL7ygvXv3KjIyUmPHjtXp0z9cGyQhIUEbNmxQYmKitm/froKCAk2cOFEVFRXOmilTpiglJUUbN27Uxo0blZKSovj4eOf+iooKTZgwQYWFhdq+fbsSExP1zjvvaN68ec6a/Px8jR07Vg6HQ3v37tWKFSu0dOlSLVu2rKndBgAAHVSTps8KCgp022236ZVXXtHjjz/u3G6M0XPPPaeHH35YkyZNkiS98cYb6tq1q95++23de++9ysvL06uvvqo///nPGjNmjCTpzTffVHR0tDZv3qzx48friy++0MaNG7Vr1y4NHTpUkvTKK69o+PDh+uqrr9SvXz8lJSXp4MGDSktLk8PhkCQ988wzmjp1qp544gkFBQXprbfeUklJidasWSOr1aqYmBgdOnRIy5Yt09y5c+Xh4XFBvzwAHVD2ocZtB9BhNGmkaObMmZowYYIz1FRLTU1VZmamxo0b59xmtVoVFxenHTt2SJKSk5NVXl7uUuNwOBQTE+Os2blzp+x2uzMQSdKwYcNkt9tdamJiYpyBSJLGjx+v0tJSJScnO2vi4uJktVpdao4fP64jR47U2rfS0lLl5+e7PAB0An5hVRdvXD9dWh1X87F+etV+v7DWbimAFtLokaLExER9+umn2rt3b419mZmZkqSuXbu6bO/atau+/fZbZ42Pj49CQkJq1FQ/PzMzUxERETVePyIiwqXm3OOEhITIx8fHpeaiiy6qcZzqfb169apxjCVLlmjRokW1dx5AxxUcXXU166JTdddwGxCgQ2tUKEpLS9OcOXOUlJQkX1/fOuvOnZYyxpx3qurcmtrqm6OmepF1Xe156KGHNHfuXOfP+fn5io7mTRDoFIKjCT1AJ9ao6bPk5GRlZWVp8ODB8vb2lre3t7Zu3arnn39e3t7eLqMwZ8vKynLui4yMVFlZmXJycuqtOXHiRI3jnzx50qXm3OPk5OSovLy83pqsrCxJNUezqlmtVgUFBbk8AABAx9eoUDR69Gjt27dPKSkpzseQIUN02223KSUlRRdffLEiIyO1adMm53PKysq0detWjRgxQpI0ePBgWSwWl5qMjAzt37/fWTN8+HDl5eVpz549zprdu3crLy/PpWb//v3KyMhw1iQlJclqtWrw4MHOmm3btrmcpp+UlCSHw1FjWg0AAHRujZo+CwwMVExMjMs2f39/hYWFObcnJCRo8eLF6tOnj/r06aPFixfLz89PU6ZMkSTZ7XZNmzZN8+bNU1hYmEJDQzV//nzFxsY6F25feumluvbaazV9+nS9/PLLkqR77rlHEydOVL9+/SRJ48aNU//+/RUfH6+nn35a3333nebPn6/p06c7R3emTJmiRYsWaerUqfrtb3+rr7/+WosXL9ajjz7KmWcAAMBFs1/R+oEHHlBxcbHuu+8+5eTkaOjQoUpKSlJgYKCz5tlnn5W3t7duvvlmFRcXa/To0VqzZo28vLycNW+99ZZmz57tPEvthhtu0AsvvODc7+Xlpffee0/33XefRo4cKZvNpilTpmjp0qXOGrvdrk2bNmnmzJkaMmSIQkJCNHfuXJc1QwAAAFIzhKKPP/7Y5WcPDw8tXLhQCxcurPM5vr6+WrFihVasWFFnTWhoqN588816j92jRw/94x//qLcmNjZW27Ztq7cGAADggm7zAQAA0FEQigAAAEQoAgAAkEQoAgAAkEQoAgAAkNQCp+QDAC7M4ayCeveH+PsoKtjmptYAnQehCADaiBB/H9ksXkpYl1Jvnc3ipc3z4ghGQDMjFAFAGxEVbNPmeXHKKSyrs+ZwVoES1qUop7CMUAQ0M0IRALQhUcE2wg7QSlhoDQAAIEaKAMD9sg/Vvc8vTAqOdl9bADgRigC0SeeegeWbXaDekg6fLNDhyvrPzmqz/MIki5+0fnrdNRY/aeYeghHQCghFANqUIJul1jOwBnik6j2rNCcxRQdMnmwWL4X4+7ROI5sqOLoq8BSdqn1/9qGqwFR0ilAEtAJCEYA2JSLAWusZWL7ZdmmDtPzWQSoJj22/1+oJjibwAG0UoQhAm1PrGVgeAZKk3l0CJIe9FVoFoKPj7DMAAAARigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACRJ3q3dAADAObIP1bnLUuDjxoYAnQuhCADaCr8wyeInrZ9eZ0kfb5sc+oMbGwV0HoQiAGgrgqOlmXukolO1788+JM/10xXicdq97QI6CUIRALQlwdFVDwBux0JrAAAAEYoAAAAkEYoAAAAksaYIANqlw1kF9e4P8fdRVLDNTa0BOgZCEQC0M77enkpYl1Jvjc3ipc3z4ghGQCMQigCgnVkVP1hZ/pfUuf9wVoES1qUop7CMUAQ0AqEIQMeRm1bvNX46iogAqyIc9tZuBtDhEIoAdAy5adKLV0jlRXXXWPyqrhoNALUgFAFwj9w0+WZ/qwEeqfLNtkseAa77L3Qkp+hUVSCa9IoU3rf2Gr8wLowIoE6EIgAt7/tRnN7lRXrPKmlDHXXNMZIT3ldyDLqw1wDQKRGKALS870dx0q5erhkbC7T81kHq3SWgZh0jOQBaEaEIgNuUBvfWAZOnkvBYiYXCANoYrmgNAAAgQhEAAIAkps8AtDd1naXWga5DBKB1EIoAtA9+YVVnp62fXncN1yECcAEIRQDah+Boaeaeuq9YLXH2GoALQigC0H4ERxN6ALQYFloDAACIUAQAACCJ6TMAzexwVkGNbb7ZBeotKe27Yvc3CAAaiFAEoFmE+PvIZvFSwrqUGvsGeKTqPau0NOkr2Sy9FeLv4/4GAsB5EIoANIuoYJs2z4tTTmFZjX2+2XZpg7T81kGy9RysqGBbK7QQAOpHKALQbKKCbbUHHo+qm7/27hIgEYgAtFGNWmi9ZMkS/fjHP1ZgYKAiIiJ044036quvvnKpMcZo4cKFcjgcstlsuuqqq3TgwAGXmtLSUt1///0KDw+Xv7+/brjhBh07dsylJicnR/Hx8bLb7bLb7YqPj1dubq5LzdGjR3X99dfL399f4eHhmj17tsrKXL+l7tu3T3FxcbLZbIqKitJjjz0mY0xjug0AADqBRoWirVu3aubMmdq1a5c2bdqkM2fOaNy4cSosLHTWPPXUU1q2bJleeOEF7d27V5GRkRo7dqxOnz7trElISNCGDRuUmJio7du3q6CgQBMnTlRFRYWzZsqUKUpJSdHGjRu1ceNGpaSkKD4+3rm/oqJCEyZMUGFhobZv367ExES98847mjdvnrMmPz9fY8eOlcPh0N69e7VixQotXbpUy5Yta9IvCwAAdGDmAmRlZRlJZuvWrcYYYyorK01kZKR58sknnTUlJSXGbrebl156yRhjTG5urrFYLCYxMdFZk56ebjw9Pc3GjRuNMcYcPHjQSDK7du1y1uzcudNIMl9++aUxxpj333/feHp6mvT0dGfN2rVrjdVqNXl5ecYYY1auXGnsdrspKSlx1ixZssQ4HA5TWVnZoD7m5eUZSc7XBDqk9M+MWRBU9c9afJ3yL2MWBFX9swVeHw3UwN/jvmO5pudv/mH2Hct1S7OAtqgpn98XdJ2ivLw8SVJoaKgkKTU1VZmZmRo3bpyzxmq1Ki4uTjt27JAkJScnq7y83KXG4XAoJibGWbNz507Z7XYNHTrUWTNs2DDZ7XaXmpiYGDkcDmfN+PHjVVpaquTkZGdNXFycrFarS83x48d15MiRC+k6AADoYJociowxmjt3rq688krFxMRIkjIzMyVJXbt2dant2rWrc19mZqZ8fHwUEhJSb01ERESNY0ZERLjUnHuckJAQ+fj41FtT/XN1zblKS0uVn5/v8gAAAB1fk0PRrFmz9Pnnn2vt2rU19nl4eLj8bIypse1c59bUVt8cNeb7RdZ1tWfJkiXOxd12u13R0dxnCQCAzqBJoej+++/X3//+d23ZskXdu3d3bo+MjJRUcxQmKyvLOUITGRmpsrIy5eTk1Ftz4sSJGsc9efKkS825x8nJyVF5eXm9NVlZWZJqjmZVe+ihh5SXl+d8pKWl1fObAOCUmyYdT6n9kX2oNVsGAA3SqFBkjNGsWbO0fv16ffTRR+rVq5fL/l69eikyMlKbNm1ybisrK9PWrVs1YsQISdLgwYNlsVhcajIyMrR//35nzfDhw5WXl6c9e/Y4a3bv3q28vDyXmv379ysjI8NZk5SUJKvVqsGDBztrtm3b5nKaflJSkhwOhy666KJa+2i1WhUUFOTyAHAeuWnSi1dIq+Nqf6yfLln8JL+w1m4pANSpURdvnDlzpt5++2393//9nwIDA52jMHa7XTabTR4eHkpISNDixYvVp08f9enTR4sXL5afn5+mTJnirJ02bZrmzZunsLAwhYaGav78+YqNjdWYMWMkSZdeeqmuvfZaTZ8+XS+//LIk6Z577tHEiRPVr18/SdK4cePUv39/xcfH6+mnn9Z3332n+fPna/r06c4gM2XKFC1atEhTp07Vb3/7W3399ddavHixHn300fNO5wFohKJTUnmRNOkVKbxv7TV+YVIw09EA2q5GhaJVq1ZJkq666iqX7a+//rqmTp0qSXrggQdUXFys++67Tzk5ORo6dKiSkpIUGBjorH/22Wfl7e2tm2++WcXFxRo9erTWrFkjLy8vZ81bb72l2bNnO89Su+GGG/TCCy8493t5eem9997Tfffdp5EjR8pms2nKlClaunSps8Zut2vTpk2aOXOmhgwZopCQEM2dO1dz585tTLcBNFR4X8kxqLVbAQBN0qhQZBpwJWgPDw8tXLhQCxcurLPG19dXK1as0IoVK+qsCQ0N1ZtvvlnvsXr06KF//OMf9dbExsZq27Zt9dYAaBhr7mHpeEDNHawZAtABcO8zAOdV4RuqImNV9JY50pY6ilgzBKCdIxQBOK/ygCiNKX1af5r8P1U3da0Na4YAtHOEIgANclzhKgmPlRz21m4KALSIC7rNBwAAQEfBSBEAtDf1LWz3C5PE9dWApiAUAUB74RdWtaB9/fS6ayx+svx/H7qvTUAHQigCgPYiOFqauafqYpm1yT4krZ8ur5Lv3NsuoIMgFAFAexIczVl+QAthoTUAAIAIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIIRQAAAJIk79ZuAACgdaTnFiunsKzemhB/H0UF29zUIqB1EYoAoBNKzy3WmGe2qri8ot46m8VLm+fFEYzQKRCKAKATyiksU3F5hZ67ZZB6RwTUWnM4q0AJ61KUU1hGKEKnQCgCgE6sd0SAYqLsrd0MoE0gFAEdRW6aVHSq7v1+YVJwtPvaAwDtDKEI6Ahy06QXr5DKi+qusfhJM/cQjACgDoQioCMoOlUViCa9IoX3rbk/+5C0fnpVHaEIAGpFKAI6kvC+kmNQa7cCANolLt4IAAAgRooASMoqKFWEpMMnC1Ri8mrsP5xV4P5GAYCbEYqATi49t1iz/5ysd7ykOYkpOlBLKJKqLuIX4u/j5tYBgPsQioBOLqewTCVnKiUvafmtg1QSHltrHbd7ANDREYoAOPXuEiA5uJBfe2fNPawBHgU6echHh7Ndg2yFb6jKA6KYEgVqQSgCOpPsQzU2+WYXqLdHeis0Bs3OL0yy+Cl6yxy9Z5W0tWZJkbFqTOnTOq5wpkSBcxCKgM7g+w9LrZ9eY1dvSct9pEpvmzz9wtzfNjSf4OiqC3QWnVJWQanyi8tddltzDyt6yxz9afL/qCQ8lilR4ByEIqAdS88tVk5hWdVoj2o/e6zqg++HD8tzHT5ZoDmJKVo2+Rr148KO7V9wtBQcrQhJEefuOx4gbWGaFKgLoQhop9JzizXmma0qLq/QAI9UvWet/ewxm8VLm+fFVQWjWkJPicnTAZOn8oAodzUdra2WaVQX3CcPnRShCGincgrLVFxeoeduGaQYT7u0oebZY4ezCpSwLkU5hWVMk6DeaVQX1ffJU5BbmgW0FYQioJ3rHRGg3h4BVf/OtAjqU880qtPZ98kjFKGTIRQBQGdSxzQqAEIRAOA8zndNI85iQ0dBKAIA1CrE30c2i5cS1qXUW/fDYn6CEdo3QhEAoFZRwTZtnhennMKyOmtYzI+OhFAEAKhTVLCNsINOw7O1GwAAANAWMFIEtAe5aTVOo/bNLtAAj1T5Ztslj+P1Pr2+hbLcGBS1qu8Cj1zcER0UoQho63LTpBevkMqLXDb3lqpu+rnh+w0Wv6oPq7M0ZqEsNwaFpIZd4LH64o4EI3QwhCKgLahlJMgp+1BVIJr0ihTe17m5+p5ly28dVHXRxlq+vTdkoazEKdU4y/ku8Hj2xR0JRehgCEVAK6i+kaskWQrS1ecv18jzTHHdT7D4ST2Gu3wIVd+zrCQ8tt6rWLNQFo3GBR7RSRGKADc7+0aukr6/mWux5pTdp8Pmh5uy+np7alX8YEUEWFnDAQBuQCgC3OGs6bHikwW6+MxhzR/fT9GhNllzA6Qt0v23THDezLX62i9Z/pcognuZAYBbEIqAlnbOQmnnAumtZ9VY/NS7Z08pmAAEAK2FUAS0tKJTLgulayyQlpgeA4A2gFAENIfznT0mVZ055hjU4AXSQHvCTWPRERCKgIaoL/QUZUvr4mtcR8hFLdcQAjoCbhqLjoRQBJxPHRdPdGHxk25/R/ILr30/02PooLhpLDoSQhFwPuesCaoVoQedGNfCQkdBKAIa6vs1QQDatrMvjloX1jihNoQiAECHce7FUevCGifUhlAEAHALd5yhllNYpuLyCj13yyD1jgiosx2scUJtCEVo++o780tq0Hqe5hpOZ1geaLzWOEOtd0SAYqLaxiUveN9oPwhFaNsaeubXzD11BqPmGk7PKijVmFXuHZav75v1+b51A21Fc56hdr6A0db+LpjOa18IRWh5FzLSc74zv7IPSeunS0d31nmMgtM+zTKcnl9c7rZh+cZ8sw7x92nycYAmq74oaW1q+ZtujjPUGhMw2srfBdN57QuhCPU7X6CR6g81zTDSI6nuM7/8wqqev356nU/t422TQ39o0HB6bd8yfbML1FtS2nfFkho2LH+hIzwN+WYtMeSOVtCAv7nz/k3X8r7im12gAR6p8s22S/49a31uQwKG1LC/C4ey5Zu9T/Ko43Wa+TIbbWk6D3UjFHUQLTJn3ZBAI9X/BljPSE9WQalKM75Q9JY5SvvPhyoN7l3j6aHFRxRa37GDo6uOXc8tNjzXT9ePPb+s9w0wvMK/zpGZAR6pes8qLU36SjZL73q/gTbnCA/XfkGb1IC/Oa2fXrW/tveEOt5XnDdK3iBVetv09U0fqTwgyqWm+gvFhQYMS0G6Nlt/Lb8NpfUUNeDLWhvE+qULQyhq73LTlJV1XLP/nKySM5U1dueYQB1X1VWWGz1n3ZCLFp7vDbDaOSM96bnFGrNqq0LKPbTZalX0ljl1PrXS2ybP+m6RERxd97H9wlTpbdNyrZQ2rKzzJSItftoyfZuyvSJq7PPNtksbpOW3DpKt5+B6f3+M8KBTqO9v7nzqeF/JKijV//45Wd0r0rRcK7XyjT/rsImq8fTBFk+FV/SV1PRQ5FXynfw8SpV29XJF9xlUs6Ch72ttDOuXLlynCEUrV67U008/rYyMDA0YMEDPPfecRo0a1drNqtKQ6am6fH/PrYjyIr3jJcmrZkn1N64viuy1z1k34kamjVH9baV66unwyQKVmDzn/sNZBSour9CSW8Yoze9jeZV8V+M10r4r1tKkr7Rs8jXq19Q3puBofX3TR5q75iPXu9Kf7fs3wEjvQkXWdoPW70eXencJkBrwJsIID9AA57yvREh6ft5lKjiRqsq/vF71RaYuf/59s4zilAb3bvoFWZvhrNjm1pzrlzrriFOHD0Xr1q1TQkKCVq5cqZEjR+rll1/Wddddp4MHD6pHjx6t27iGTk/Vo9Lbpl3DV+uJj0/W/ND/fuqoX2CZygNq+QNp6HqfhtzI9KxFl1kFpc6Rq94e6VruI81JTNGBs0KRVPVt5ce9QhUVXPPboCSVpOfpwAdlNYbQz3Xes1GK7Dpgep3/rvR1LRytb0EpgGYTFWyTgvtLs/Y2fXpOOm9gseYellT1xaskPa/G/uovc98d3V/79H1DbwJ9y5/lW+j7w1qps6fvmyM0ndPP6nVZMZ529fYIaPIxOvOIU4cPRcuWLdO0adP0y1/+UpL03HPP6YMPPtCqVau0ZMmS1m3c98PIaVcvr3U9jSTlFZdr8Xtf1Do1Jkk5JYE6viVANotdtp6Dax/JyD4kX1P1x3LykI8OZ1fVWHMPK/o8x6/wDVV5YZBUWPONQ5IsBT7q422T51mLLiMkl5GrSm+blk2+pka4aei3jPoWJp8qLNOMPydf2NkoDV04yl3ugYa7kC8ZFzI914Ave9GSioxVD39wXMc/2F5jv0PZ2my1KnTjzLqP8/1NoLMqA5VfXO6yy7v4O/XYfI883/yFy1qpGs+/5c+SX7gzzJz9/lzhG1r/e2Yt/axxrCaui2rMiNPe1O+UU8+i94ZoSyNOHToUlZWVKTk5WQ8++KDL9nHjxmnHjh21Pqe0tFSlpT8svsvLqwoD+fn5zd6+k5nfqUup0bT/O6kvjX+ddb6Wrnru1ssV6mepsybYz0eBnuXKzz/rj/OMj1ThK639pSIkrZWkJNfnZRof/eJvJcpURh2vnCHpQL39iNRCBXucdm2zt6eevfVydfH3kWyh6hbcvZZnntPec3hXlMinskSz/1T7fyvnsSyeWtmU3081T7sU/6FUXHMKz8kWWlXXAv8fAB3KWe87dfK2VdU15e/pdIFUaqQjKVX/fq5Th6WCQun656Ww2r/sSdKJM/5a5hVe5/6NR9fppY2faPY1fRQVUvMDu9I3RCfyw5WQ+JlKymt+aT37fdHl/VCq+kK8/h7p1UmSVOv7c5HxUUL5ffrOBDq3+Xp7at74fgrytcia9191LyjUsVF/UKn9YklSek6xnv/oa/3hF5fpfzwypHdnSye+rXrv+l7B6XxVlhbp8/9mqOB07b///54sVGVpkSJtleoR6FFrjXeFd4PenxvC1+Kpv8+6Uo5mDkbVn9vGmIY/yXRg6enpRpL597//7bL9iSeeMH379q31OQsWLDCSePDgwYMHDx4d4JGWltbg3NChR4qqeXi4Jl1jTI1t1R566CHNnTvX+XNlZaW+++47hYWF1fmctiw/P1/R0dFKS0tTUFBQazfHreh75+t7Z+23RN/pO30/lzFGp0+flsPhaPBrduhQFB4eLi8vL2VmZrpsz8rKUteuXWt9jtVqldVqddkWHBzcUk10m6CgoE73B1ONvne+vnfWfkv0nb53PvX13W63N+q1PJujQW2Vj4+PBg8erE2bNrls37Rpk0aMGNFKrQIAAG1Rhx4pkqS5c+cqPj5eQ4YM0fDhw7V69WodPXpUM2bMaO2mAQCANqTDh6JbbrlFp06d0mOPPaaMjAzFxMTo/fffV8+ePVu7aW5htVq1YMGCGlOCnQF973x976z9lug7fafvzcHDmMacqwYAANAxdeg1RQAAAA1FKAIAABChCAAAQBKhCAAAQBKhqENYuXKlevXqJV9fXw0ePFj/+te/6qydOnWqPDw8ajwGDBjgxhY3n8b0XZLeeustDRw4UH5+furWrZvuuusunTpV992026rG9vvFF1/UpZdeKpvNpn79+ulPf/qTm1ravLZt26brr79eDodDHh4e+tvf/nbe52zdulWDBw+Wr6+vLr74Yr300kst39AW0Ni+Z2RkaMqUKerXr588PT2VkJDglna2hMb2ff369Ro7dqy6dOmioKAgDR8+XB988IF7GtuMGtvv7du3a+TIkQoLC5PNZtMll1yiZ5991j2NbWZN+Vuv9u9//1ve3t4aNGhQo49LKGrn1q1bp4SEBD388MP67LPPNGrUKF133XU6evRorfXLly9XRkaG85GWlqbQ0FDddNNNbm75hWts37dv36477rhD06ZN04EDB/SXv/xFe/fu1S9/Wc+NK9ugxvZ71apVeuihh7Rw4UIdOHBAixYt0syZM/Xuu++6ueUXrrCwUAMHDtQLL7zQoPrU1FT99Kc/1ahRo/TZZ5/pt7/9rWbPnq133nmnhVva/Brb99LSUnXp0kUPP/ywBg4c2MKta1mN7fu2bds0duxYvf/++0pOTtbVV1+t66+/Xp999lkLt7R5Nbbf/v7+mjVrlrZt26YvvvhCjzzyiB555BGtXr26hVva/Brb92p5eXm64447NHr06KYduMl3W0WbcMUVV5gZM2a4bLvkkkvMgw8+2KDnb9iwwXh4eJgjR460RPNaVGP7/vTTT5uLL77YZdvzzz9vunfv3mJtbAmN7ffw4cPN/PnzXbbNmTPHjBw5ssXa6A6SzIYNG+qteeCBB8wll1zisu3ee+81w4YNa8GWtbyG9P1scXFxZs6cOS3WHndqbN+r9e/f3yxatKj5G+QmTe33z3/+c3P77bc3f4PcqDF9v+WWW8wjjzxiFixYYAYOHNjoYzFS1I6VlZUpOTlZ48aNc9k+btw47dixo0Gv8eqrr2rMmDHt7mKWTen7iBEjdOzYMb3//vsyxujEiRP661//qgkTJrijyc2iKf0uLS2Vr6+vyzabzaY9e/aovLy8xdraFuzcubPG72r8+PH65JNPOnzf8YPKykqdPn1aoaGhrd0Ut/rss8+0Y8cOxcXFtXZT3OL111/XN998owULFjT5NQhF7Vh2drYqKipq3Ny2a9euNW6CW5uMjAz985//bHfTR1LT+j5ixAi99dZbuuWWW+Tj46PIyEgFBwdrxYoV7mhys2hKv8ePH68//vGPSk5OljFGn3zyiV577TWVl5crOzvbHc1uNZmZmbX+rs6cOdPh+44fPPPMMyosLNTNN9/c2k1xi+7du8tqtWrIkCGaOXNmu3yPb6yvv/5aDz74oN566y15ezf9Zh2Eog7Aw8PD5WdjTI1ttVmzZo2Cg4N14403tlDLWl5j+n7w4EHNnj1bjz76qJKTk7Vx40alpqa2y/vgNabfv/vd73Tddddp2LBhslgs+tnPfqapU6dKkry8vFq6qa2utt9VbdvRMa1du1YLFy7UunXrFBER0drNcYt//etf+uSTT/TSSy/pueee09q1a1u7SS2qoqJCU6ZM0aJFi9S3b98Leq0Of++zjiw8PFxeXl41RgiysrJqfDs+lzFGr732muLj4+Xj49OSzWwRTen7kiVLNHLkSP3617+WJF122WXy9/fXqFGj9Pjjj6tbt24t3u4L1ZR+22w2vfbaa3r55Zd14sQJdevWTatXr1ZgYKDCw8Pd0exWExkZWevvytvbW2FhYa3UKrjLunXrNG3aNP3lL3/RmDFjWrs5btOrVy9JUmxsrE6cOKGFCxdq8uTJrdyqlnP69Gl98skn+uyzzzRr1ixJVVOmxhh5e3srKSlJ11xzTYNei5GidszHx0eDBw/Wpk2bXLZv2rRJI0aMqPe5W7du1eHDhzVt2rSWbGKLaUrfi4qK5Onp+r989UiJaSe3ALyQ/+YWi0Xdu3eXl5eXEhMTNXHixBq/j45m+PDhNX5XSUlJGjJkiCwWSyu1Cu6wdu1aTZ06VW+//Xa7WjfY3IwxKi0tbe1mtKigoCDt27dPKSkpzseMGTPUr18/paSkaOjQoQ1+LUaK2rm5c+cqPj5eQ4YM0fDhw7V69WodPXrUOSX00EMPKT09vcZ1aV599VUNHTpUMTExrdHsZtHYvl9//fWaPn26Vq1apfHjxysjI0MJCQm64oor5HA4WrMrjdLYfh86dEh79uzR0KFDlZOTo2XLlmn//v164403WrMbTVJQUKDDhw87f05NTVVKSopCQ0PVo0ePGn2fMWOGXnjhBc2dO1fTp0/Xzp079eqrr7bL6YTG9l2SUlJSnM89efKkUlJS5OPjo/79+7u7+ReksX1fu3at7rjjDi1fvlzDhg1zjhbabDbZ7fZW6UNTNLbfL774onr06KFLLrlEUtVlSJYuXar777+/Vdp/IRrTd09PzxqfZREREfL19W38Z1yjz1dDm/Piiy+anj17Gh8fH/OjH/3IbN261bnvzjvvNHFxcS71ubm5xmazmdWrV7u5pc2vsX1//vnnTf/+/Y3NZjPdunUzt912mzl27JibW33hGtPvgwcPmkGDBhmbzWaCgoLMz372M/Pll1+2Qqsv3JYtW4ykGo8777zTGFP7f/OPP/7YXH755cbHx8dcdNFFZtWqVe5veDNoSt9rq+/Zs6fb236hGtv3uLi4euvbi8b2+/nnnzcDBgwwfn5+JigoyFx++eVm5cqVpqKionU6cAGa8v/72Zp6Sr6HMe1k3gAAAKAFdewFBQAAAA1EKAIAABChCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQJL0/wMI78hqWjKrdQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2042\n",
      "working on HD 302 out of 2042\n",
      "working on HD 604 out of 2042\n",
      "working on HD 925 out of 2042\n",
      "working on HD 1229 out of 2042\n",
      "working on HD 1534 out of 2042\n",
      "working on HD 1840 out of 2042\n",
      "all done checking HD and complement contiguity for all 2042 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 4\n",
      "overpop contigFail, enclaveFail = 0 0 out of 182\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 1646 with pop 251617.0 now has use 0.97339\n",
      "CCB unit 1647 with pop 103725.0 now has use 0.98587\n",
      "CCB unit 1648 with pop 373177.0 now has use 0.93228\n",
      "CCB unit 1649 with pop 284018.0 now has use 0.9747\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"unpatchedContigGoodPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv ./2024state_HD_output/MD2042unpatchedContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2042 HD lists of units\n",
      "orig unit avg and sd usage are 0.99936 0.07755\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 800\n",
      "working on HD 1600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 300\n",
      "working on HD 600\n",
      "working on HD 900\n",
      "working on HD 1200\n",
      "working on HD 1500\n",
      "working on HD 1800\n",
      "working on HD 2100\n",
      "out of 2151 total HDs, there were 2151 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "1646 0.97339 251617.0\n",
      "1647 0.9784 103725.0\n",
      "1648 0.92738 373177.0\n",
      "1649 0.9747 284018.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.95\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMinUse value 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 442 units out of 1650 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38607\n",
      "current avg and SD of unit usage are 0.99936 0.07755 . Now trying to increase up to 15 units' underusage\n",
      "all done trying to reduce usage of unit 735 final usage = 0.87075 68 sec elapsed 58 202 successful, failed patches, couldn't start= 55\n",
      "current avg and SD of usage are 0.99938 0.07538\n",
      "all done trying to reduce usage of unit 778 final usage = 0.77513 75 sec elapsed 29 20 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99939 0.07366\n",
      "all done trying to reduce usage of unit 1640 final usage = 0.86989 81 sec elapsed 25 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.9994 0.07205\n",
      "all done trying to reduce usage of unit 1607 final usage = 0.87074 88 sec elapsed 23 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99939 0.07107\n",
      "all done trying to reduce usage of unit 1604 final usage = 0.97682 94 sec elapsed 54 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99938 0.06708\n",
      "all done trying to reduce usage of unit 1615 final usage = 0.97498 107 sec elapsed 41 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99939 0.06483\n",
      "all done trying to reduce usage of unit 726 final usage = 0.78986 122 sec elapsed 22 118 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9994 0.06369\n",
      "all done trying to reduce usage of unit 775 final usage = 0.80702 125 sec elapsed 10 22 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99941 0.0626\n",
      "all done trying to reduce usage of unit 625 final usage = 0.87156 131 sec elapsed 24 25 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99941 0.06131\n",
      "starting to increase usage for unit 621 with usage 0.73694 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 621 final usage = 0.85549 143 sec elapsed 30 27 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 0.99942 0.05998\n",
      "all done trying to reduce usage of unit 1644 final usage = 0.97288 153 sec elapsed 28 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99942 0.05885\n",
      "all done trying to reduce usage of unit 618 final usage = 0.84105 156 sec elapsed 9 10 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99942 0.05847\n",
      "all done trying to reduce usage of unit 731 final usage = 0.94373 175 sec elapsed 34 119 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99944 0.05757\n",
      "all done trying to reduce usage of unit 1639 final usage = 0.97467 184 sec elapsed 27 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99945 0.057\n",
      "all done trying to reduce usage of unit 616 final usage = 0.84987 193 sec elapsed 11 37 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99945 0.05652\n"
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overpopped first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99936 0.99945 and their SDs are 0.07755 0.05652\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2042\n",
      "working on HD 300 out of 2042\n",
      "working on HD 600 out of 2042\n",
      "working on HD 900 out of 2042\n",
      "working on HD 1200 out of 2042\n",
      "working on HD 1500 out of 2042\n",
      "working on HD 1800 out of 2042\n",
      "all done checking HD and complement contiguity for all 2042 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.08\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 296 units out of 1650 with usage above 1.05\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38607\n",
      "current avg and SD of unit usage are 0.99945 0.05652 . Now trying to reduce up to 296 units' overusage\n",
      "starting to reduce usage for unit 562 with usage 1.18861 . Total units tried = 1\n",
      "try to drop unit 562 from 24 th HD.  usage down to 1.1611934422322119\n",
      "try to drop unit 562 from 48 th HD.  usage down to 1.100133004728251\n",
      "all done trying to reduce usage of unit 562 final usage = 1.04676 4 sec elapsed 34 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99945 0.05423\n",
      "starting to reduce usage for unit 1618 with usage 1.13237 . Total units tried = 2\n",
      "all done trying to reduce usage of unit 1618 final usage = 1.04205 5 sec elapsed 14 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99945 0.05327\n",
      "starting to reduce usage for unit 279 with usage 1.11447 . Total units tried = 3\n",
      "try to drop unit 279 from 22 th HD.  usage down to 1.0664155938005981\n",
      "all done trying to reduce usage of unit 279 final usage = 1.04526 8 sec elapsed 26 5 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99946 0.05238\n",
      "starting to reduce usage for unit 1596 with usage 1.1028 . Total units tried = 4\n",
      "try to drop unit 1596 from 15 th HD.  usage down to 1.0563113787033527\n",
      "all done trying to reduce usage of unit 1596 final usage = 1.04756 10 sec elapsed 15 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99946 0.05174\n",
      "starting to reduce usage for unit 242 with usage 1.10184 . Total units tried = 5\n",
      "try to drop unit 242 from 20 th HD.  usage down to 1.0732290103126225\n",
      "all done trying to reduce usage of unit 242 final usage = 1.04609 12 sec elapsed 22 2 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99946 0.05101\n",
      "starting to reduce usage for unit 238 with usage 1.10096 . Total units tried = 6\n",
      "try to drop unit 238 from 22 th HD.  usage down to 1.0508318947150752\n",
      "all done trying to reduce usage of unit 238 final usage = 1.04354 14 sec elapsed 19 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99946 0.05028\n",
      "starting to reduce usage for unit 241 with usage 1.09035 . Total units tried = 7\n",
      "try to drop unit 241 from 24 th HD.  usage down to 1.0641686297921755\n",
      "try to drop unit 241 from 48 th HD.  usage down to 1.0524805317079884\n",
      "all done trying to reduce usage of unit 241 final usage = 1.04776 16 sec elapsed 14 9 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99947 0.04971\n",
      "starting to reduce usage for unit 945 with usage 1.08325 . Total units tried = 8\n",
      "all done trying to reduce usage of unit 945 final usage = 1.04934 21 sec elapsed 10 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99947 0.04937\n",
      "starting to reduce usage for unit 1178 with usage 1.08109 . Total units tried = 9\n",
      "all done trying to reduce usage of unit 1178 final usage = 1.04821 24 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99947 0.04899\n",
      "starting to reduce usage for unit 1169 with usage 1.0788 . Total units tried = 10\n",
      "try to drop unit 1169 from 16 th HD.  usage down to 1.0648951697395597\n",
      "all done trying to reduce usage of unit 1169 final usage = 1.049 26 sec elapsed 11 11 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99947 0.04865\n",
      "starting to reduce usage for unit 380 with usage 1.07206 . Total units tried = 11\n",
      "try to drop unit 380 from 21 th HD.  usage down to 1.0540747753358708\n",
      "all done trying to reduce usage of unit 380 final usage = 1.04855 27 sec elapsed 7 12 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99947 0.04844\n",
      "starting to reduce usage for unit 1337 with usage 1.06732 . Total units tried = 12\n",
      "all done trying to reduce usage of unit 1337 final usage = 1.04705 29 sec elapsed 4 4 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99947 0.04821\n",
      "starting to reduce usage for unit 1295 with usage 1.07782 . Total units tried = 13\n",
      "all done trying to reduce usage of unit 1295 final usage = 1.04861 31 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99947 0.04786\n",
      "starting to reduce usage for unit 120 with usage 1.06571 . Total units tried = 14\n",
      "try to drop unit 120 from 23 th HD.  usage down to 1.0522500074466046\n",
      "all done trying to reduce usage of unit 120 final usage = 1.04936 42 sec elapsed 8 18 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99948 0.04772\n",
      "starting to reduce usage for unit 1464 with usage 1.06507 . Total units tried = 15\n",
      "all done trying to reduce usage of unit 1464 final usage = 1.0492 42 sec elapsed 3 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99947 0.04756\n",
      "starting to reduce usage for unit 165 with usage 1.06475 . Total units tried = 16\n",
      "all done trying to reduce usage of unit 165 final usage = 1.04938 43 sec elapsed 6 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99948 0.04737\n",
      "starting to reduce usage for unit 1455 with usage 1.06383 . Total units tried = 17\n",
      "all done trying to reduce usage of unit 1455 final usage = 1.04643 44 sec elapsed 3 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99947 0.04709\n",
      "starting to reduce usage for unit 705 with usage 1.06309 . Total units tried = 18\n",
      "all done trying to reduce usage of unit 705 final usage = 1.04667 44 sec elapsed 3 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99947 0.04696\n",
      "starting to reduce usage for unit 1395 with usage 1.06284 . Total units tried = 19\n",
      "all done trying to reduce usage of unit 1395 final usage = 1.04991 45 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99947 0.04683\n",
      "starting to reduce usage for unit 1312 with usage 1.0613 . Total units tried = 20\n",
      "all done trying to reduce usage of unit 1312 final usage = 1.04819 46 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99947 0.04675\n",
      "starting to reduce usage for unit 1595 with usage 1.0716 . Total units tried = 21\n",
      "all done trying to reduce usage of unit 1595 final usage = 1.043 46 sec elapsed 5 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99948 0.04644\n",
      "starting to reduce usage for unit 1293 with usage 1.061 . Total units tried = 22\n",
      "all done trying to reduce usage of unit 1293 final usage = 1.04633 47 sec elapsed 3 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99948 0.04629\n",
      "starting to reduce usage for unit 940 with usage 1.06026 . Total units tried = 23\n",
      "all done trying to reduce usage of unit 940 final usage = 1.04924 48 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99948 0.04618\n",
      "starting to reduce usage for unit 897 with usage 1.0601 . Total units tried = 24\n",
      "all done trying to reduce usage of unit 897 final usage = 1.04976 49 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99948 0.0461\n",
      "starting to reduce usage for unit 289 with usage 1.06065 . Total units tried = 25\n",
      "all done trying to reduce usage of unit 289 final usage = 1.04856 50 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.046\n",
      "starting to reduce usage for unit 706 with usage 1.05942 . Total units tried = 26\n",
      "all done trying to reduce usage of unit 706 final usage = 1.04818 51 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04587\n",
      "starting to reduce usage for unit 906 with usage 1.05888 . Total units tried = 27\n",
      "all done trying to reduce usage of unit 906 final usage = 1.04891 52 sec elapsed 2 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04578\n",
      "starting to reduce usage for unit 169 with usage 1.05704 . Total units tried = 28\n",
      "all done trying to reduce usage of unit 169 final usage = 1.04685 54 sec elapsed 5 4 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99949 0.0457\n",
      "starting to reduce usage for unit 1194 with usage 1.0577 . Total units tried = 29\n",
      "all done trying to reduce usage of unit 1194 final usage = 1.04856 55 sec elapsed 3 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04556\n",
      "starting to reduce usage for unit 978 with usage 1.05796 . Total units tried = 30\n",
      "all done trying to reduce usage of unit 978 final usage = 1.04915 56 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04549\n",
      "starting to reduce usage for unit 1118 with usage 1.05513 . Total units tried = 31\n",
      "all done trying to reduce usage of unit 1118 final usage = 1.04803 57 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04544\n",
      "starting to reduce usage for unit 917 with usage 1.05414 . Total units tried = 32\n",
      "all done trying to reduce usage of unit 917 final usage = 1.04884 57 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99949 0.0454\n",
      "starting to reduce usage for unit 977 with usage 1.05342 . Total units tried = 33\n",
      "all done trying to reduce usage of unit 977 final usage = 1.04988 60 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99949 0.04537\n",
      "starting to reduce usage for unit 201 with usage 1.05491 . Total units tried = 34\n",
      "all done trying to reduce usage of unit 201 final usage = 1.04762 61 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04525\n",
      "starting to reduce usage for unit 214 with usage 1.05395 . Total units tried = 35\n",
      "all done trying to reduce usage of unit 214 final usage = 1.04796 61 sec elapsed 2 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.0452\n",
      "starting to reduce usage for unit 1207 with usage 1.05312 . Total units tried = 36\n",
      "try to drop unit 1207 from 12 th HD.  usage down to 1.0531203012873251\n",
      "all done trying to reduce usage of unit 1207 final usage = 1.04893 63 sec elapsed 2 12 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04517\n",
      "starting to reduce usage for unit 942 with usage 1.05287 . Total units tried = 37\n",
      "all done trying to reduce usage of unit 942 final usage = 1.04708 63 sec elapsed 3 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04512\n",
      "starting to reduce usage for unit 1313 with usage 1.05291 . Total units tried = 38\n",
      "all done trying to reduce usage of unit 1313 final usage = 1.04748 64 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04508\n",
      "starting to reduce usage for unit 687 with usage 1.05211 . Total units tried = 39\n",
      "all done trying to reduce usage of unit 687 final usage = 1.04733 64 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04504\n",
      "starting to reduce usage for unit 297 with usage 1.05332 . Total units tried = 40\n",
      "all done trying to reduce usage of unit 297 final usage = 1.04901 65 sec elapsed 2 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04501\n",
      "starting to reduce usage for unit 944 with usage 1.05198 . Total units tried = 41\n",
      "all done trying to reduce usage of unit 944 final usage = 1.04956 65 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.045\n",
      "starting to reduce usage for unit 1542 with usage 1.05179 . Total units tried = 42\n",
      "all done trying to reduce usage of unit 1542 final usage = 1.04457 66 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04491\n",
      "starting to reduce usage for unit 1242 with usage 1.05231 . Total units tried = 43\n",
      "all done trying to reduce usage of unit 1242 final usage = 1.04675 69 sec elapsed 2 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.9995 0.04484\n",
      "starting to reduce usage for unit 62 with usage 1.0516 . Total units tried = 44\n",
      "all done trying to reduce usage of unit 62 final usage = 1.04994 69 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04483\n",
      "starting to reduce usage for unit 1066 with usage 1.05274 . Total units tried = 45\n",
      "all done trying to reduce usage of unit 1066 final usage = 1.0457 70 sec elapsed 1 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04477\n",
      "starting to reduce usage for unit 1124 with usage 1.05115 . Total units tried = 46\n",
      "all done trying to reduce usage of unit 1124 final usage = 1.04944 71 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.04476\n",
      "starting to reduce usage for unit 804 with usage 1.05058 . Total units tried = 47\n",
      "all done trying to reduce usage of unit 804 final usage = 1.04347 71 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04466\n",
      "starting to reduce usage for unit 65 with usage 1.05064 . Total units tried = 48\n",
      "all done trying to reduce usage of unit 65 final usage = 1.04137 72 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.0446\n",
      "starting to reduce usage for unit 960 with usage 1.05003 . Total units tried = 49\n",
      "all done trying to reduce usage of unit 960 final usage = 1.04443 74 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1238 with usage 1.04981 . Total units tried = 50\n",
      "all done trying to reduce usage of unit 1238 final usage = 1.04981 74 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1351 with usage 1.04972 . Total units tried = 51\n",
      "all done trying to reduce usage of unit 1351 final usage = 1.04972 74 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1206 with usage 1.04965 . Total units tried = 52\n",
      "all done trying to reduce usage of unit 1206 final usage = 1.04965 75 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1286 with usage 1.04958 . Total units tried = 53\n",
      "all done trying to reduce usage of unit 1286 final usage = 1.04958 75 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 272 with usage 1.04954 . Total units tried = 54\n",
      "all done trying to reduce usage of unit 272 final usage = 1.04954 76 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1108 with usage 1.04892 . Total units tried = 55\n",
      "all done trying to reduce usage of unit 1108 final usage = 1.04892 76 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 366 with usage 1.04888 . Total units tried = 56\n",
      "all done trying to reduce usage of unit 366 final usage = 1.04888 76 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1456 with usage 1.04875 . Total units tried = 57\n",
      "all done trying to reduce usage of unit 1456 final usage = 1.04875 76 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 243 with usage 1.04996 . Total units tried = 58\n",
      "all done trying to reduce usage of unit 243 final usage = 1.04996 77 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04457\n",
      "starting to reduce usage for unit 1557 with usage 1.05039 . Total units tried = 59\n",
      "all done trying to reduce usage of unit 1557 final usage = 1.04661 77 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 147 with usage 1.04843 . Total units tried = 60\n",
      "all done trying to reduce usage of unit 147 final usage = 1.04843 77 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 565 with usage 1.04841 . Total units tried = 61\n",
      "all done trying to reduce usage of unit 565 final usage = 1.04841 77 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 292 with usage 1.04838 . Total units tried = 62\n",
      "all done trying to reduce usage of unit 292 final usage = 1.04838 78 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 921 with usage 1.0483 . Total units tried = 63\n",
      "all done trying to reduce usage of unit 921 final usage = 1.0483 78 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 901 with usage 1.0483 . Total units tried = 64\n",
      "all done trying to reduce usage of unit 901 final usage = 1.0483 78 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 86 with usage 1.04786 . Total units tried = 65\n",
      "all done trying to reduce usage of unit 86 final usage = 1.04786 79 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1283 with usage 1.04781 . Total units tried = 66\n",
      "all done trying to reduce usage of unit 1283 final usage = 1.04781 79 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 943 with usage 1.04762 . Total units tried = 67\n",
      "all done trying to reduce usage of unit 943 final usage = 1.04762 79 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1162 with usage 1.04741 . Total units tried = 68\n",
      "all done trying to reduce usage of unit 1162 final usage = 1.04741 80 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 128 with usage 1.04966 . Total units tried = 69\n",
      "all done trying to reduce usage of unit 128 final usage = 1.04966 80 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1064 with usage 1.04734 . Total units tried = 70\n",
      "all done trying to reduce usage of unit 1064 final usage = 1.04734 80 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 753 with usage 1.04731 . Total units tried = 71\n",
      "all done trying to reduce usage of unit 753 final usage = 1.04731 81 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1177 with usage 1.0472 . Total units tried = 72\n",
      "all done trying to reduce usage of unit 1177 final usage = 1.0472 81 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 822 with usage 1.04851 . Total units tried = 73\n",
      "all done trying to reduce usage of unit 822 final usage = 1.04851 81 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 283 with usage 1.04703 . Total units tried = 74\n",
      "all done trying to reduce usage of unit 283 final usage = 1.04703 82 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1231 with usage 1.04689 . Total units tried = 75\n",
      "all done trying to reduce usage of unit 1231 final usage = 1.04689 82 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 71 with usage 1.04676 . Total units tried = 76\n",
      "all done trying to reduce usage of unit 71 final usage = 1.04676 82 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 188 with usage 1.04666 . Total units tried = 77\n",
      "all done trying to reduce usage of unit 188 final usage = 1.04666 83 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 92 with usage 1.04659 . Total units tried = 78\n",
      "all done trying to reduce usage of unit 92 final usage = 1.04659 83 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 919 with usage 1.04656 . Total units tried = 79\n",
      "all done trying to reduce usage of unit 919 final usage = 1.04656 83 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 927 with usage 1.04652 . Total units tried = 80\n",
      "all done trying to reduce usage of unit 927 final usage = 1.04652 84 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 974 with usage 1.04651 . Total units tried = 81\n",
      "all done trying to reduce usage of unit 974 final usage = 1.04651 84 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1102 with usage 1.0465 . Total units tried = 82\n",
      "all done trying to reduce usage of unit 1102 final usage = 1.0465 84 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1340 with usage 1.04644 . Total units tried = 83\n",
      "all done trying to reduce usage of unit 1340 final usage = 1.04644 85 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 296 with usage 1.04642 . Total units tried = 84\n",
      "all done trying to reduce usage of unit 296 final usage = 1.04642 85 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1020 with usage 1.04637 . Total units tried = 85\n",
      "all done trying to reduce usage of unit 1020 final usage = 1.04637 85 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 986 with usage 1.04634 . Total units tried = 86\n",
      "all done trying to reduce usage of unit 986 final usage = 1.04634 86 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 923 with usage 1.04631 . Total units tried = 87\n",
      "all done trying to reduce usage of unit 923 final usage = 1.04631 86 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1291 with usage 1.04611 . Total units tried = 88\n",
      "all done trying to reduce usage of unit 1291 final usage = 1.04611 86 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 290 with usage 1.0471 . Total units tried = 89\n",
      "all done trying to reduce usage of unit 290 final usage = 1.0471 87 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1167 with usage 1.046 . Total units tried = 90\n",
      "all done trying to reduce usage of unit 1167 final usage = 1.046 87 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 153 with usage 1.04594 . Total units tried = 91\n",
      "all done trying to reduce usage of unit 153 final usage = 1.04594 87 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1306 with usage 1.04591 . Total units tried = 92\n",
      "all done trying to reduce usage of unit 1306 final usage = 1.04591 87 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1220 with usage 1.04591 . Total units tried = 93\n",
      "all done trying to reduce usage of unit 1220 final usage = 1.04591 88 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 828 with usage 1.04585 . Total units tried = 94\n",
      "all done trying to reduce usage of unit 828 final usage = 1.04585 88 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 196 with usage 1.04583 . Total units tried = 95\n",
      "all done trying to reduce usage of unit 196 final usage = 1.04583 88 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1451 with usage 1.04583 . Total units tried = 96\n",
      "all done trying to reduce usage of unit 1451 final usage = 1.04583 89 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 276 with usage 1.04581 . Total units tried = 97\n",
      "all done trying to reduce usage of unit 276 final usage = 1.04581 89 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 842 with usage 1.04566 . Total units tried = 98\n",
      "all done trying to reduce usage of unit 842 final usage = 1.04566 89 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 191 with usage 1.04564 . Total units tried = 99\n",
      "all done trying to reduce usage of unit 191 final usage = 1.04564 89 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 490 with usage 1.0455 . Total units tried = 100\n",
      "all done trying to reduce usage of unit 490 final usage = 1.0455 90 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1309 with usage 1.04549 . Total units tried = 101\n",
      "all done trying to reduce usage of unit 1309 final usage = 1.04549 90 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 568 with usage 1.04545 . Total units tried = 102\n",
      "all done trying to reduce usage of unit 568 final usage = 1.04545 90 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1211 with usage 1.04541 . Total units tried = 103\n",
      "all done trying to reduce usage of unit 1211 final usage = 1.04541 91 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1314 with usage 1.04735 . Total units tried = 104\n",
      "all done trying to reduce usage of unit 1314 final usage = 1.04735 91 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1385 with usage 1.04529 . Total units tried = 105\n",
      "all done trying to reduce usage of unit 1385 final usage = 1.04529 91 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1317 with usage 1.04525 . Total units tried = 106\n",
      "all done trying to reduce usage of unit 1317 final usage = 1.04525 91 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 717 with usage 1.04523 . Total units tried = 107\n",
      "all done trying to reduce usage of unit 717 final usage = 1.04523 91 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1000 with usage 1.04518 . Total units tried = 108\n",
      "all done trying to reduce usage of unit 1000 final usage = 1.04518 92 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1405 with usage 1.04509 . Total units tried = 109\n",
      "all done trying to reduce usage of unit 1405 final usage = 1.04509 92 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1187 with usage 1.04504 . Total units tried = 110\n",
      "all done trying to reduce usage of unit 1187 final usage = 1.04504 92 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1224 with usage 1.045 . Total units tried = 111\n",
      "all done trying to reduce usage of unit 1224 final usage = 1.045 93 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1123 with usage 1.04499 . Total units tried = 112\n",
      "all done trying to reduce usage of unit 1123 final usage = 1.04499 93 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 281 with usage 1.04481 . Total units tried = 113\n",
      "all done trying to reduce usage of unit 281 final usage = 1.04481 93 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 24 with usage 1.0448 . Total units tried = 114\n",
      "all done trying to reduce usage of unit 24 final usage = 1.0448 94 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 898 with usage 1.04479 . Total units tried = 115\n",
      "all done trying to reduce usage of unit 898 final usage = 1.04479 94 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 959 with usage 1.04479 . Total units tried = 116\n",
      "all done trying to reduce usage of unit 959 final usage = 1.04479 94 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 968 with usage 1.04464 . Total units tried = 117\n",
      "all done trying to reduce usage of unit 968 final usage = 1.04464 95 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1241 with usage 1.04463 . Total units tried = 118\n",
      "all done trying to reduce usage of unit 1241 final usage = 1.04463 95 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1200 with usage 1.0446 . Total units tried = 119\n",
      "all done trying to reduce usage of unit 1200 final usage = 1.0446 95 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1089 with usage 1.04457 . Total units tried = 120\n",
      "all done trying to reduce usage of unit 1089 final usage = 1.04457 96 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 25 with usage 1.04454 . Total units tried = 121\n",
      "all done trying to reduce usage of unit 25 final usage = 1.04454 96 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 949 with usage 1.04444 . Total units tried = 122\n",
      "all done trying to reduce usage of unit 949 final usage = 1.04444 96 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 989 with usage 1.04442 . Total units tried = 123\n",
      "all done trying to reduce usage of unit 989 final usage = 1.04442 97 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1343 with usage 1.04438 . Total units tried = 124\n",
      "all done trying to reduce usage of unit 1343 final usage = 1.04438 97 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 957 with usage 1.04437 . Total units tried = 125\n",
      "all done trying to reduce usage of unit 957 final usage = 1.04437 97 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 270 with usage 1.04436 . Total units tried = 126\n",
      "all done trying to reduce usage of unit 270 final usage = 1.04436 98 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 903 with usage 1.04423 . Total units tried = 127\n",
      "all done trying to reduce usage of unit 903 final usage = 1.04423 98 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n",
      "starting to reduce usage for unit 1201 with usage 1.04423 . Total units tried = 128\n",
      "all done trying to reduce usage of unit 1201 final usage = 1.04423 98 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.04454\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[175], line 30\u001b[0m\n\u001b[0;32m     27\u001b[0m startTime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m currSD \u001b[38;5;241m>\u001b[39m maxSD \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(attemptedBigUs) \u001b[38;5;241m<\u001b[39m maxNtries: \n\u001b[0;32m     29\u001b[0m     \u001b[38;5;66;03m#each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\u001b[39;00m\n\u001b[1;32m---> 30\u001b[0m     idx \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(unitUse)\n\u001b[0;32m     31\u001b[0m     idxNo, nBigFound, bigUsers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m,\u001b[38;5;28mlist\u001b[39m()\n\u001b[0;32m     32\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m nBigFound \u001b[38;5;241m<\u001b[39m nBigUsers:\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36margsort\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:1146\u001b[0m, in \u001b[0;36margsort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m   1038\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_argsort_dispatcher)\n\u001b[0;32m   1039\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21margsort\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1040\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1041\u001b[0m \u001b[38;5;124;03m    Returns the indices that would sort an array.\u001b[39;00m\n\u001b[0;32m   1042\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1144\u001b[0m \n\u001b[0;32m   1145\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1146\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapfunc(a, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124margsort\u001b[39m\u001b[38;5;124m'\u001b[39m, axis\u001b[38;5;241m=\u001b[39maxis, kind\u001b[38;5;241m=\u001b[39mkind, order\u001b[38;5;241m=\u001b[39morder)\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:54\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     52\u001b[0m bound \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, method, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bound \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     57\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m bound(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:43\u001b[0m, in \u001b[0;36m_wrapit\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m     42\u001b[0m     wrap \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(asarray(obj), method)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wrap:\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, mu\u001b[38;5;241m.\u001b[39mndarray):\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run first and third for MA\n",
    "maxSD = 0.04 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "90492bd1-3320-4712-a1be-0fc321c682bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.97\n",
      "this would currently cover a total of 228 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 150\n",
      "enter updated stopMinUse value for ending usage boost on a unit 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 228 units out of 1650 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 23164\n",
      "current avg and SD of unit usage are 0.99949 0.04454 . Now trying to increase up to 150 units' underusage\n",
      "all done trying to reduce usage of unit 699 final usage = 0.97145 5 sec elapsed 35 32 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.9995 0.04374\n",
      "all done trying to reduce usage of unit 775 final usage = 0.84636 7 sec elapsed 2 21 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9995 0.04363\n",
      "all done trying to reduce usage of unit 778 final usage = 0.93854 14 sec elapsed 27 19 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99951 0.04306\n",
      "all done trying to reduce usage of unit 726 final usage = 0.89843 22 sec elapsed 28 100 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99952 0.04238\n",
      "all done trying to reduce usage of unit 727 final usage = 0.9712 30 sec elapsed 38 39 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99953 0.0417\n",
      "all done trying to reduce usage of unit 1611 final usage = 0.97236 36 sec elapsed 23 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99953 0.04131\n",
      "all done trying to reduce usage of unit 618 final usage = 0.8655 38 sec elapsed 5 18 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99954 0.04108\n",
      "all done trying to reduce usage of unit 617 final usage = 0.88308 41 sec elapsed 9 6 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99953 0.04079\n",
      "all done trying to reduce usage of unit 866 final usage = 0.88738 46 sec elapsed 9 11 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99954 0.04057\n",
      "starting to increase usage for unit 1606 with usage 0.86625 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 1606 final usage = 0.97485 50 sec elapsed 26 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99955 0.03991\n",
      "all done trying to reduce usage of unit 1279 final usage = 0.97492 56 sec elapsed 30 1 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99958 0.03911\n",
      "all done trying to reduce usage of unit 745 final usage = 0.97346 62 sec elapsed 26 27 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99958 0.03859\n",
      "all done trying to reduce usage of unit 1638 final usage = 0.97341 66 sec elapsed 17 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99959 0.0383\n",
      "all done trying to reduce usage of unit 1636 final usage = 0.97484 72 sec elapsed 18 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99959 0.03815\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[178], line 147\u001b[0m\n\u001b[0;32m    145\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[u] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap \u001b[38;5;129;01mand\u001b[39;00m u \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m shedCandidates:\n\u001b[0;32m    146\u001b[0m         shedCandidates\u001b[38;5;241m.\u001b[39mappend(u)  \u001b[38;5;66;03m#we'll check enclavity if ever picked\u001b[39;00m\n\u001b[1;32m--> 147\u001b[0m         shedScores\u001b[38;5;241m.\u001b[39mappend((unitUse[u] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1.\u001b[39m) \u001b[38;5;241m*\u001b[39m unitCP[u]\u001b[38;5;241m.\u001b[39mdistance(hdCP[t])\u001b[38;5;241m/\u001b[39mavgDist[t])\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m kkk, u \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(shedCandidates): \u001b[38;5;66;03m#checking if this shed eliminates high-pop future sheds ...\u001b[39;00m\n\u001b[0;32m    149\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[u] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap:\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\geometry\\base.py:334\u001b[0m, in \u001b[0;36mBaseGeometry.distance\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdistance\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m    333\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Unitless distance to other geometry (float)\"\"\"\u001b[39;00m\n\u001b[1;32m--> 334\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_unpack(shapely\u001b[38;5;241m.\u001b[39mdistance(\u001b[38;5;28mself\u001b[39m, other))\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\decorators.py:77\u001b[0m, in \u001b[0;36mmultithreading_enabled.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m array_args:\n\u001b[0;32m     76\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     79\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m arr, old_flag \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(array_args, old_flags):\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\shapely\\measurement.py:72\u001b[0m, in \u001b[0;36mdistance\u001b[1;34m(a, b, **kwargs)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[38;5;129m@multithreading_enabled\u001b[39m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdistance\u001b[39m(a, b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     49\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Computes the Cartesian distance between two geometries.\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \n\u001b[0;32m     51\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;124;03m    nan\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mdistance(a, b, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99936 0.9996 and their SDs are 0.07755 0.03709\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2042\n",
      "working on HD 300 out of 2042\n",
      "working on HD 600 out of 2042\n",
      "working on HD 900 out of 2042\n",
      "working on HD 1200 out of 2042\n",
      "working on HD 1500 out of 2042\n",
      "working on HD 1800 out of 2042\n",
      "all done checking HD and complement contiguity for all 2042 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211 7213 7211 7211 7211 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999075 2042\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "8 1 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999055\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+ioqK5HK5fFtCQkIg0wQAAEGkVx9gdTgcfn8bYzq1SVJbW5tmzZqlRx99VGPHju3x/RcWFsrj8fi22tra3kwTAAAEgZ4dqvj/oqKiFBIS0ukoSH19faejJZLU1NSkPXv2qLKyUgsXLpQktbe3yxijoUOH6p133tGUKVM6jXM6nXI6nYFMDQAABKmAjoyEhYUpNTVV5eXlfu3l5eXKyMjo1D8yMlL79u1TVVWVb8vLy9OPfvQjVVVVafLkyd9t9gAAIOgFdGREkgoKCjRnzhylpaUpPT1dzz77rGpqapSXlyfpq1Msn376qV566SUNGTJEycnJfuOjo6MVHh7eqR0AAAxOAYeRnJwcNTY2asWKFXK73UpOTlZZWZkSExMlSW63+6zXHAEAAOgQ8HVGbOA6IwAABJ9zcp0RAACAvkYYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVr8JIcXGxkpKSFB4ertTUVG3btq3bvuvXr9d1112niy66SJGRkUpPT9fbb7/d6wkDAICBJeAwUlpaqvz8fC1btkyVlZXKzMxUdna2ampquuy/detWXXfddSorK1NFRYWuvfZaTZs2TZWVld958gAAIPg5jDEmkAGTJ09WSkqKVq9e7WsbN26cpk+frqKioh7dx/jx45WTk6OHH364R/29Xq9cLpc8Ho8iIyMDmS4AALCkp+/fAR0ZaWlpUUVFhbKysvzas7KytGPHjh7dR3t7u5qamjRixIhu+zQ3N8vr9fptAABgYAoojDQ0NKitrU0xMTF+7TExMaqrq+vRffzxj3/UyZMnNWPGjG77FBUVyeVy+baEhIRApgkAAIJIrz7A6nA4/P42xnRq68orr7yiRx55RKWlpYqOju62X2FhoTwej2+rra3tzTQBAEAQGBpI56ioKIWEhHQ6ClJfX9/paMk3lZaWav78+Xrttdf0y1/+8lv7Op1OOZ3OQKYGAACCVEBHRsLCwpSamqry8nK/9vLycmVkZHQ77pVXXtG8efP017/+VTfddFPvZgoAAAakgI6MSFJBQYHmzJmjtLQ0paen69lnn1VNTY3y8vIkfXWK5dNPP9VLL70k6asgkpubqz/96U+64oorfEdVhg0bJpfL1YelAACAYBRwGMnJyVFjY6NWrFght9ut5ORklZWVKTExUZLkdrv9rjmyZs0anTlzRgsWLNCCBQt87XPnztULL7zw3SsAAABBLeDrjNjAdUYAAAg+5+Q6IwAAAH2NMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq4bangAAoGfa2o3er/5M9U2nFR0RrklJIxQyxGF7WjgPBvra9yqMFBcX68knn5Tb7db48eP11FNPKTMzs9v+W7ZsUUFBgfbv36/4+Hj9z//8j/Ly8no9aQAYbDZ+6Naj/+eA3J7TvrY4V7iWT/uxbkiOszgznGuDYe0DPk1TWlqq/Px8LVu2TJWVlcrMzFR2drZqamq67F9dXa0bb7xRmZmZqqys1IMPPqhFixbp9ddf/86TB4DBYOOHbv325b1+b0aSVOc5rd++vFcbP3RbmhnOtcGy9g5jjAlkwOTJk5WSkqLVq1f72saNG6fp06erqKioU/+lS5fqzTff1MGDB31teXl5+uCDD7Rz584e7dPr9crlcsnj8SgyMjKQ6QJAUGtrN7rq93/v9GbUwSEp1hWu7UunDKjD9hgYa9/T9++Ajoy0tLSooqJCWVlZfu1ZWVnasWNHl2N27tzZqf/111+vPXv2qLW1tcsxzc3N8nq9fhsADEbvV3/W7ZuRJBlJbs9pvV/92fmbFM6LwbT2AYWRhoYGtbW1KSYmxq89JiZGdXV1XY6pq6vrsv+ZM2fU0NDQ5ZiioiK5XC7flpCQEMg0AWDAqG/q/s2oN/0QPAbT2vfqq70Oh//hIGNMp7az9e+qvUNhYaE8Ho9vq62t7c00ASDoRUeE92k/BI/BtPYBfZsmKipKISEhnY6C1NfXdzr60SE2NrbL/kOHDtXIkSO7HON0OuV0OgOZGgAMSJOSRijOFa46z2l19QG/js8NTEoacb6nhnNsMK19QEdGwsLClJqaqvLycr/28vJyZWRkdDkmPT29U/933nlHaWlpCg0NDXC6ADC4hAxxaPm0H0v66s3n6zr+Xj7tx/32A4zovcG09gGfpikoKNBzzz2nkpISHTx4UIsXL1ZNTY3vuiGFhYXKzc319c/Ly9ORI0dUUFCggwcPqqSkRGvXrtWSJUv6rgoAGMBuSI7T6l+nKNblfzg+1hWu1b9OGTDXmkBng2XtA77oWU5OjhobG7VixQq53W4lJyerrKxMiYmJkiS32+13zZGkpCSVlZVp8eLFevrppxUfH68///nPuu222/quCgAY4G5IjtN1P44d0FfhRNcGw9oHfJ0RG7jOCAAAweecXGcEAACgrxFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYFfDl4GzouEuv1ei3PBAAA9FTH+/bZLvYeFGGkqalJkpSQkGB5JgAAIFBNTU1yuVzd3h4Uv03T3t6uY8eOKSIiQg5H3/0wkNfrVUJCgmprawfkb94M5PqoLTgN5NqkgV0ftQUn27UZY9TU1KT4+HgNGdL9J0OC4sjIkCFDNGrUqHN2/5GRkQPuAfh1A7k+agtOA7k2aWDXR23ByWZt33ZEpAMfYAUAAFYRRgAAgFWDOow4nU4tX75cTqfT9lTOiYFcH7UFp4FcmzSw66O24BQstQXFB1gBAMDANaiPjAAAAPsIIwAAwCrCCAAAsIowAgAArBrUYaS4uFhJSUkKDw9Xamqqtm3bZntK36qoqEg/+9nPFBERoejoaE2fPl0fffSRX5958+bJ4XD4bVdccYVfn+bmZt1zzz2KiorS8OHDdfPNN+vo0aPns5QuPfLII53mHhsb67vdGKNHHnlE8fHxGjZsmK655hrt37/f7z76a22XXHJJp9ocDocWLFggKbjWbevWrZo2bZri4+PlcDi0YcMGv9v7ap0+//xzzZkzRy6XSy6XS3PmzNGJEyes1dba2qqlS5dqwoQJGj58uOLj45Wbm6tjx4753cc111zTaS1nzpxpvbaz1Sf13eOwv62dpC6ffw6HQ08++aSvT39cu5687gfzc67DoA0jpaWlys/P17Jly1RZWanMzExlZ2erpqbG9tS6tWXLFi1YsEC7du1SeXm5zpw5o6ysLJ08edKv3w033CC32+3bysrK/G7Pz8/XG2+8oVdffVXbt2/XF198oalTp6qtre18ltOl8ePH+8193759vtueeOIJrVy5UqtWrdLu3bsVGxur6667zvfbRVL/rW337t1+dZWXl0uSbr/9dl+fYFm3kydPauLEiVq1alWXt/fVOs2aNUtVVVXauHGjNm7cqKqqKs2ZM8dabadOndLevXv10EMPae/evVq/fr0+/vhj3XzzzZ363n333X5ruWbNGr/bbdQmnX3tpL55HPa3tZPkV5Pb7VZJSYkcDoduu+02v379be168rofzM85HzNITZo0yeTl5fm1XXbZZeaBBx6wNKPA1dfXG0lmy5Ytvra5c+eaW265pdsxJ06cMKGhoebVV1/1tX366admyJAhZuPGjedyume1fPlyM3HixC5va29vN7Gxsebxxx/3tZ0+fdq4XC7zzDPPGGP6d23fdO+995oxY8aY9vZ2Y0zwrpsk88Ybb/j+7qt1OnDggJFkdu3a5euzc+dOI8n861//OsdVfeWbtXXl/fffN5LMkSNHfG1XX321uffee7sd0x9qM6br+vricdgf6uvJ2t1yyy1mypQpfm3BsHbffN0fKM+5QXlkpKWlRRUVFcrKyvJrz8rK0o4dOyzNKnAej0eSNGLECL/2zZs3Kzo6WmPHjtXdd9+t+vp6320VFRVqbW31qz0+Pl7Jycn9ovZDhw4pPj5eSUlJmjlzpg4fPixJqq6uVl1dnd+8nU6nrr76at+8+3ttHVpaWvTyyy/rzjvv9Pvhx2Betw59tU47d+6Uy+XS5MmTfX2uuOIKuVyuflWvx+ORw+HQhRde6Ne+bt06RUVFafz48VqyZInf/1D7e23f9XHY3+uTpOPHj+utt97S/PnzO93W39fum6/7A+U5FxQ/lNfXGhoa1NbWppiYGL/2mJgY1dXVWZpVYIwxKigo0FVXXaXk5GRfe3Z2tm6//XYlJiaqurpaDz30kKZMmaKKigo5nU7V1dUpLCxM3//+9/3urz/UPnnyZL300ksaO3asjh8/rscee0wZGRnav3+/b25drdmRI0ckqV/X9nUbNmzQiRMnNG/ePF9bMK/b1/XVOtXV1Sk6OrrT/UdHR/ebek+fPq0HHnhAs2bN8vsBstmzZyspKUmxsbH68MMPVVhYqA8++MB3aq4/19YXj8P+XF+HF198UREREbr11lv92vv72nX1uj9QnnODMox0+Pr/SqWvFvqbbf3VwoUL9c9//lPbt2/3a8/JyfH9Ozk5WWlpaUpMTNRbb73V6Yn3df2h9uzsbN+/J0yYoPT0dI0ZM0Yvvvii70N0vVmz/lDb161du1bZ2dmKj4/3tQXzunWlL9apq/79pd7W1lbNnDlT7e3tKi4u9rvt7rvv9v07OTlZl156qdLS0rR3716lpKRI6r+19dXjsL/W16GkpESzZ89WeHi4X3t/X7vuXve7mlewPecG5WmaqKgohYSEdEp79fX1ndJlf3TPPffozTff1KZNmzRq1Khv7RsXF6fExEQdOnRIkhQbG6uWlhZ9/vnnfv36Y+3Dhw/XhAkTdOjQId+3ar5tzYKhtiNHjujdd9/VXXfd9a39gnXd+mqdYmNjdfz48U73/9///td6va2trZoxY4aqq6tVXl5+1p9lT0lJUWhoqN9a9tfavqk3j8P+Xt+2bdv00UcfnfU5KPWvtevudX+gPOcGZRgJCwtTamqq79Bbh/LycmVkZFia1dkZY7Rw4UKtX79ef//735WUlHTWMY2NjaqtrVVcXJwkKTU1VaGhoX61u91uffjhh/2u9ubmZh08eFBxcXG+Q6dfn3dLS4u2bNnim3cw1Pb8888rOjpaN91007f2C9Z166t1Sk9Pl8fj0fvvv+/r849//EMej8dqvR1B5NChQ3r33Xc1cuTIs47Zv3+/WltbfWvZX2vrSm8eh/29vrVr1yo1NVUTJ048a9/+sHZne90fMM+5c/4R2X7q1VdfNaGhoWbt2rXmwIEDJj8/3wwfPtx88skntqfWrd/+9rfG5XKZzZs3G7fb7dtOnTpljDGmqanJ3HfffWbHjh2murrabNq0yaSnp5uLL77YeL1e3/3k5eWZUaNGmXfffdfs3bvXTJkyxUycONGcOXPGVmnGGGPuu+8+s3nzZnP48GGza9cuM3XqVBMREeFbk8cff9y4XC6zfv16s2/fPvOrX/3KxMXFBUVtxhjT1tZmRo8ebZYuXerXHmzr1tTUZCorK01lZaWRZFauXGkqKyt93yjpq3W64YYbzOWXX2527txpdu7caSZMmGCmTp1qrbbW1lZz8803m1GjRpmqqiq/52Bzc7Mxxph///vf5tFHHzW7d+821dXV5q233jKXXXaZ+elPf2q9trPV15ePw/62dh08Ho+54IILzOrVqzuN769rd7bXfWOC+znXYdCGEWOMefrpp01iYqIJCwszKSkpfl+R7Y8kdbk9//zzxhhjTp06ZbKyssxFF11kQkNDzejRo83cuXNNTU2N3/18+eWXZuHChWbEiBFm2LBhZurUqZ362JCTk2Pi4uJMaGioiY+PN7feeqvZv3+/7/b29nazfPlyExsba5xOp/n5z39u9u3b53cf/bU2Y4x5++23jSTz0Ucf+bUH27pt2rSpy8fh3LlzjTF9t06NjY1m9uzZJiIiwkRERJjZs2ebzz//3Fpt1dXV3T4HN23aZIwxpqamxvz85z83I0aMMGFhYWbMmDFm0aJFprGx0XptZ6uvLx+H/W3tOqxZs8YMGzbMnDhxotP4/rp2Z3vdNya4n3MdHMYYc44OugAAAJzVoPzMCAAA6D8IIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKz6fzgdeA25yCVqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for MD\n",
      "In year/election 2020 Dem and GOP total votes were 1984904 976351 for a stateGOP of 0.32971\n",
      "There are a total of 2042  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1677884 943134 for a stateGOP of 0.35983\n",
      "There are a total of 2042  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for MD\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv MD2042patchedVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 2042 drawn districts.  This state has 8 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c38b293-7a46-4106-a972-b4868206bc2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Rep-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Rep-leaning vtds for year\",years[0])  #for MA, do GOP-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] < RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"R\", color = \"red\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 1984904 976351 -1008552\n",
      "ensemble : 1981407 967833 -1013573\n",
      "the true and ensemble state GOP leans are 0.32971 0.3281636476393892\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 2158 2157 9\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
